def __init__(self, batch_size, vocab_size, left_context, right_context,
emb_size, k, unigram, l1_weight=0, l2_weight=0, nce_seed=2345):
self.name = 'vLBL'
self.batch_size = batch_size
self.vocab_size = vocab_size
self.left_context = left_context
self.right_context = right_context
self.context_size = self.left_context + self.right_context
self.emb_size = emb_size
self.k = k
self.unigram = unigram
self.p_n = debug_print(theano.shared(value=unigram, name='noise_probab'),
'noise')
self.l1_weight = l1_weight
self.l2_weight = l2_weight
self.nce_seed = nce_seed
# Create context and target embeddings
rand_values = random_value_normal((self.vocab_size, self.emb_size),
floatX, np.random.RandomState(1234))
self.R = theano.shared(value=rand_values, name='R')
rand_values = random_value_normal((self.vocab_size, self.emb_size),
floatX, np.random.RandomState(4321))
self.Q = theano.shared(value=rand_values, name='Q')
b_values = zero_value((self.vocab_size,), dtype=floatX)
self.bias = theano.shared(value=b_values, name='bias')
# The learning rates are created the first time set_learning_rate is
# called.
self.lr = None
def __init__(self, learning_rate=0.2, n_epochs=2000, nkerns=[6, 14], batch_size=10, useAllSamples=0, ktop=4, filter_size=[7,5],
L2_weight=0.00005, dropout_p=0.8, useEmb=0, task=2, corpus=1, dataMode=3, maxSentLength=60, sentEm_length=48, window=3,
k=5, nce_seeds=2345, only_left_context=False, vali_cost_list_length=20, embedding_size=48, train_scheme=1):
self.ini_learning_rate=learning_rate
self.n_epochs=n_epochs
self.nkerns=nkerns
self.batch_size=batch_size
self.useAllSamples=useAllSamples
self.ktop=ktop
self.filter_size=filter_size
self.L2_weight=L2_weight
self.dropout_p=dropout_p
self.useEmb=useEmb
self.task=task
self.corpus=corpus
self.dataMode=dataMode
self.maxSentLength=maxSentLength
self.kmax=self.maxSentLength/2+5
self.sentEm_length=sentEm_length
self.window=window
self.k=k
self.only_left_context=only_left_context
if self.only_left_context:
self.context_size=self.window
else:
self.context_size=2*self.window
self.nce_seed=nce_seeds
self.embedding_size=0
self.train_scheme=train_scheme
root="/mounts/data/proj/wenpeng/Dataset/StanfordSentiment/stanfordSentimentTreebank/"
wiki_path="/mounts/data/proj/wenpeng/PhraseEmbedding/enwiki-20130503-pages-articles-cleaned-tokenized"
embeddingPath='/mounts/data/proj/wenpeng/Downloads/hlbl-embeddings-original.EMBEDDING_SIZE=50.txt'
embeddingPath2='/mounts/data/proj/wenpeng/MC/src/released_embedding.txt'
datasets, unigram, train_lengths, dev_lengths, word_count=load_model_for_training(wiki_path, root+str(self.task)+'classes/'+str(self.corpus)+'train.txt', root+str(self.task)+'classes/'+str(self.corpus)+'dev.txt',self.maxSentLength, self.dataMode, self.train_scheme)
self.datasets=datasets
self.embedding_size=embedding_size
self.vocab_size=word_count
rand_values=random_value_normal((self.vocab_size+1, self.embedding_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(self.embedding_size))
self.embeddings_R=theano.shared(value=rand_values)
rand_values=random_value_normal((self.vocab_size+1, self.embedding_size), theano.config.floatX, numpy.random.RandomState(4321))
rand_values[0]=numpy.array(numpy.zeros(self.embedding_size))
self.embeddings_Q=theano.shared(value=rand_values)
self.unigram=unigram
self.p_n=theano.shared(value=self.unigram)
self.train_lengths=train_lengths
self.dev_lengths=dev_lengths
b_values = zero_value((len(unigram),), dtype=theano.config.floatX)
self.bias = theano.shared(value=b_values, name='bias')
self.vali_cost_list_length=vali_cost_list_length
def new_load_glove(self, target_id2word):
word2embeddings={}
read_file=open('/mounts/data/proj/wenpeng/Dataset/embeddings-scaled.EMBEDDING_SIZE=200.txt')
for line in read_file:
tokens=line.strip().split()
self.target_embedding_size=len(tokens)-1
embedding=[]
for i in range(1, self.target_embedding_size+1):
embedding.append(float(tokens[i]))
word2embeddings[tokens[0]]=embedding
words_number=len(target_id2word)
print 'totally '+str(words_number)+' distinct target words'
embedding_Q=random_value_normal((len(target_id2word), self.target_embedding_size), theano.config.floatX, numpy.random.RandomState(4321))
#for i in range(100):
# embedding_Q[0][i]=0.6
unknown_words=0
for index in range(words_number):
embed=word2embeddings.get(target_id2word[index], -1)
embed_lowercase=word2embeddings.get(target_id2word[index].lower(), -1)
if embed==-1 and embed_lowercase==-1: # a unknown word which has no embedding in glove
embedding_Q[index]=numpy.array(numpy.random.rand(self.target_embedding_size))
unknown_words+=1
#print target_id2word[index]
elif embed!=-1:
embedding_Q[index]=numpy.array(embed)
else:
embedding_Q[index]=numpy.array(embed_lowercase)
print 'Collobert embeddings loaded over, '+str(unknown_words)+' words find no embeddings.'
#numpy.savetxt('matrix.txt', embedding_Q, delimiter=',')
#exit(0)
embedding_R=random_value_normal((self.trigram_size+1, self.context_embedding_size), theano.config.floatX, numpy.random.RandomState(1234))
'''
count=0
for word, embedding in word2embeddings.iteritems():
embedding_R[count]=numpy.array(embedding[:self.context_embedding_size])
count+=1
if count==(self.trigram_size+1):
break
'''
return embedding_R, embedding_Q
def __init__(self,
batch_size,
vocab_size,
left_context,
right_context,
emb_size,
k,
unigram,
l1_weight=0,
l2_weight=0,
nce_seed=2345):
self.name = 'vLBL'
self.batch_size = batch_size
self.vocab_size = vocab_size
self.left_context = left_context
self.right_context = right_context
self.context_size = self.left_context + self.right_context
self.emb_size = emb_size
self.k = k
self.unigram = unigram
self.p_n = debug_print(
theano.shared(value=unigram, name='noise_probab'), 'noise')
self.l1_weight = l1_weight
self.l2_weight = l2_weight
self.nce_seed = nce_seed
# Create context and target embeddings
rand_values = random_value_normal((self.vocab_size, self.emb_size),
floatX, np.random.RandomState(1234))
self.R = theano.shared(value=rand_values, name='R')
rand_values = random_value_normal((self.vocab_size, self.emb_size),
floatX, np.random.RandomState(4321))
self.Q = theano.shared(value=rand_values, name='Q')
b_values = zero_value((self.vocab_size, ), dtype=floatX)
self.bias = theano.shared(value=b_values, name='bias')
# The learning rates are created the first time set_learning_rate is
# called.
self.lr = None
def load_params(self, base_filename, params_str):
super(VLblNceDistributional, self).load_params(base_filename, params_str)
#if 'D' in self.__dict__:
# self.D2 = theano.sparse.basic.dense_from_sparse(self.D)
# do not re-initialize R if it is loaded - so here we check whether
#dimensions of R are right
if 'R' not in self.__dict__ \
or self.R.shape.eval()[0] != self.D.shape.eval()[1] :
rand_values = random_value_normal((self.D.shape.eval()[1], \
self.emb_size), floatX, np.random.RandomState(999))
self.R = theano.shared(value=rand_values, name='R')
def load_params(self, base_filename, params_str):
super(VLblNceDistributional, self).load_params(base_filename,
params_str)
#if 'D' in self.__dict__:
# self.D2 = theano.sparse.basic.dense_from_sparse(self.D)
# do not re-initialize R if it is loaded - so here we check whether
#dimensions of R are right
if 'R' not in self.__dict__ \
or self.R.shape.eval()[0] != self.D.shape.eval()[1] :
rand_values = random_value_normal((self.D.shape.eval()[1], \
self.emb_size), floatX, np.random.RandomState(999))
self.R = theano.shared(value=rand_values, name='R')
def evaluate_lenet5(learning_rate=0.085, n_epochs=2000, nkerns=[1,1], batch_size=1, window_width=3,
maxSentLength=60, emb_size=300, L2_weight=0.0005, update_freq=1, unifiedWidth_conv0=8, k_dy=3, ktop=3):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_msr_corpus(rootPath+'vocab.txt', rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength)
mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
#mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_number_matching_scores.txt', rootPath+'test_number_matching_scores.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
#mts=T.dmatrix()
#wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv0=ishape[1]+filter_size[1]-1
poolsize1=(1, length_after_wideConv0)
length_after_wideConv1=unifiedWidth_conv0+filter_size[1]-1
poolsize2=(1, length_after_wideConv1)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_ll=Conv_Fold_DynamicK_PoolLayer_NAACL(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), poolsize=poolsize1, k=k_dy, unifiedWidth=unifiedWidth_conv0, left=left_l, right=right_l,
W=conv_W, b=conv_b,
firstLayer=True)
layer0_rr=Conv_Fold_DynamicK_PoolLayer_NAACL(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), poolsize=poolsize1, k=k_dy, unifiedWidth=unifiedWidth_conv0, left=left_r, right=right_r,
W=conv_W, b=conv_b,
firstLayer=True)
layer0_l_output=debug_print(layer0_ll.fold_output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_rr.fold_output, 'layer0_r.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=ishape[0]/2,
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
conv_W2, conv_b2=create_conv_para(rng, filter_shape=(1, 1, filter_size[0]/2, filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer1_ll=Conv_Fold_DynamicK_PoolLayer_NAACL(rng, input=layer0_ll.output,
image_shape=(batch_size, nkerns[0], ishape[0]/2, unifiedWidth_conv0),
filter_shape=(nkerns[1], nkerns[0], filter_size[0]/2, filter_size[1]), poolsize=poolsize2, k=ktop, unifiedWidth=ktop, left=layer0_ll.leftPad, right=layer0_ll.rightPad,
W=conv_W2, b=conv_b2,
firstLayer=False)
layer1_rr=Conv_Fold_DynamicK_PoolLayer_NAACL(rng, input=layer0_rr.output,
image_shape=(batch_size, nkerns[0], ishape[0]/2, unifiedWidth_conv0),
filter_shape=(nkerns[1], nkerns[0], filter_size[0]/2, filter_size[1]), poolsize=poolsize2, k=ktop, unifiedWidth=ktop, left=layer0_rr.leftPad, right=layer0_rr.rightPad,
W=conv_W2, b=conv_b2,
firstLayer=False)
layer1_l_output=debug_print(layer1_ll.fold_output, 'layer1_l.output')
layer1_r_output=debug_print(layer1_rr.fold_output, 'layer1_r.output')
layer2=Average_Pooling_for_Top(rng, input_l=layer1_l_output, input_r=layer1_r_output, kern=ishape[0]/4,
left_l=layer0_ll.leftPad, right_l=layer0_ll.rightPad, left_r=layer0_rr.leftPad, right_r=layer0_rr.rightPad,
length_l=k_dy+filter_size[1]-1, length_r=k_dy+filter_size[1]-1,
dim=unifiedWidth_conv0+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([#mts,
eucli_1, uni_cosine,
#norm_uni_l, norm_uni_r,#uni_cosine,#norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.output_eucli_to_simi,layer1.output_cosine,
layer1.output_attentions, #layer1.output_cosine,layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
#layer1.output_vector_l,layer1.output_vector_r,
layer2.output_eucli_to_simi,layer2.output_cosine,
layer2.output_attentions,
#layer2.output_vector_l,layer2.output_vector_r,
len_l, len_r
#layer1.output_attentions,
#wmf,
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=(2)+(2+4*4)+(2+4*4)+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum()+(conv_W2**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
test_model = theano.function([index], [layer3.errors(y), layer3.y_pred, layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index]
#mts: mt_test[index: index + batch_size],
#wmf: wm_test[index: index + batch_size]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W]+[conv_W2]# + layer1.params
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
#grad_i=debug_print(grad_i,'grad_i')
#norm=T.sqrt((grad_i**2).sum())
#if T.lt(norm_threshold, norm):
# print 'big norm'
# grad_i=grad_i*(norm_threshold/norm)
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], [cost,layer3.errors(y), layer3_input], updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index]
#mts: mt_train[index: index + batch_size],
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index]
#mts: mt_train[index: index + batch_size],
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'cost_ij: ', cost_ij
cost_tmp+=cost_ij
error_sum+=error_ij
else:
cost_average, error_ij, layer3_input= train_model(batch_start,cost_tmp)
#print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' sum error: '+str(error_sum)+'/'+str(update_freq)
error_sum=0
cost_tmp=0.0#reset for the next batch
#print layer3_input
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
for i in test_batch_start:
test_loss, pred_y, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc=1-test_score
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
(1-test_score) * 100.))
#now, see the results of svm
#write_feature=open('feature_check.txt', 'w')
train_y=[]
train_features=[]
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(' '.join(map(str,layer3_input[0]))+'\n')
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
lr=linear_model.LogisticRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
corr_count=0
corr_lr=0
test_size=len(test_y)
for i in range(test_size):
if results[i]==test_y[i]:
corr_count+=1
if numpy.absolute(results_lr[i]-test_y[i])<0.5:
corr_lr+=1
acc=corr_count*1.0/test_size
acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_epoch=epoch
if acc_lr> max_acc:
max_acc=acc_lr
best_epoch=epoch
if test_acc> max_acc:
max_acc=test_acc
best_epoch=epoch
print '\t\t\t\t\t\t\t\t\t\t\tsvm acc: ', acc, 'LR acc: ', acc_lr, ' max acc: ', max_acc , ' at epoch: ', best_epoch
#exit(0)
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=500, test_batch_size=1000, emb_size=300, hidden_size=300, HL_hidden_size=200,
L2_weight=0.0001, train_size=None, test_size=None, batch_size_pred=1000,
para_len=60, question_len=20, c_len=7, e_len=2):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/work/hs/yin/20161219/';
storePath='/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id={}
word2id['UNK']=0 # use it to pad
word2id, train_questions,train_questions_mask,train_paras,train_paras_mask,train_e_ids,train_e_masks,train_c_ids,train_c_masks, train_c_heads,train_c_tails,train_l_heads,train_l_tails,train_e_heads,train_e_tails,train_labels, train_labels_3c=load_SQUAD_hinrich_v2(train_size, para_len, question_len, e_len, c_len, word2id, rootPath+'squadnewtrn.txt')
word2id, test_questions,test_questions_mask,test_paras,test_paras_mask,test_e_ids,test_e_masks,test_c_ids,test_c_masks, test_c_heads,test_c_tails,test_l_heads,test_l_tails,test_e_heads,test_e_tails,test_labels, test_labels_3c=load_SQUAD_hinrich_v2(test_size, para_len, question_len, e_len, c_len,word2id, rootPath+'squadnewdev.txt')
print 'word2id size for bigger dataset:', len(word2id)
word2id, train_questions,train_questions_mask,train_paras,train_paras_mask,train_e_ids,train_e_masks,train_c_ids,train_c_masks, train_c_heads,train_c_tails,train_l_heads,train_l_tails,train_e_heads,train_e_tails,train_labels, train_labels_3c=load_SQUAD_hinrich_v2(train_size, para_len, question_len,e_len, c_len, word2id, rootPath+'squadnewtrn,subset.000.txt')
word2id, test_questions,test_questions_mask,test_paras,test_paras_mask,test_e_ids,test_e_masks,test_c_ids,test_c_masks, test_c_heads,test_c_tails,test_l_heads,test_l_tails,test_e_heads,test_e_tails,test_labels, test_labels_3c=load_SQUAD_hinrich_v2(test_size, para_len, question_len, e_len, c_len,word2id, rootPath+'squadnewdev,subset.000.txt')
print 'word2id size for smaller dataset:', len(word2id)
# if len(train_questions)!=train_size or len(test_questions)!=test_size:
# print 'len(questions)!=train_size or len(test_questions)!=test_size:', len(train_questions),train_size,len(test_questions),test_size
# exit(0)
train_size=len(train_questions)
test_size = len(test_questions)
train_questions = np.asarray(train_questions, dtype='int32')
# print train_questions[:10,:]
# exit(0)
train_questions_mask = np.asarray(train_questions_mask, dtype=theano.config.floatX)
train_paras = np.asarray(train_paras, dtype='int32')
train_paras_mask = np.asarray(train_paras_mask, dtype=theano.config.floatX)
train_e_ids = np.asarray(train_e_ids, dtype='int32')
train_e_masks = np.asarray(train_e_masks, dtype=theano.config.floatX)
train_c_ids = np.asarray(train_c_ids, dtype='int32')
train_c_masks = np.asarray(train_c_masks, dtype=theano.config.floatX)
train_c_heads = np.asarray(train_c_heads, dtype='int32')
train_c_tails = np.asarray(train_c_tails, dtype='int32')
train_l_heads = np.asarray(train_l_heads, dtype='int32')
train_l_tails = np.asarray(train_l_tails, dtype='int32')
train_e_heads = np.asarray(train_e_heads, dtype='int32')
train_e_tails = np.asarray(train_e_tails, dtype='int32')
train_labels = np.asarray(train_labels, dtype='int32')
train_labels_3c = np.asarray(train_labels_3c, dtype='int32')
test_questions = np.asarray(test_questions, dtype='int32')
test_questions_mask = np.asarray(test_questions_mask, dtype=theano.config.floatX)
test_paras = np.asarray(test_paras, dtype='int32')
test_paras_mask = np.asarray(test_paras_mask, dtype=theano.config.floatX)
test_e_ids = np.asarray(test_e_ids, dtype='int32')
test_e_masks = np.asarray(test_e_masks, dtype=theano.config.floatX)
test_c_ids = np.asarray(test_c_ids, dtype='int32')
test_c_masks = np.asarray(test_c_masks, dtype=theano.config.floatX)
test_c_heads = np.asarray(test_c_heads, dtype='int32')
test_c_tails = np.asarray(test_c_tails, dtype='int32')
test_l_heads = np.asarray(test_l_heads, dtype='int32')
test_l_tails = np.asarray(test_l_tails, dtype='int32')
test_e_heads = np.asarray(test_e_heads, dtype='int32')
test_e_tails = np.asarray(test_e_tails, dtype='int32')
test_labels = np.asarray(test_labels, dtype='int32')
overall_vocab_size=len(word2id)
print 'train size:', train_size, 'test size:', test_size, 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
para=T.imatrix() #(2*batch, len)
para_mask=T.fmatrix() #(2*batch, len)
c_ids=T.imatrix() #(2*batch, len)
c_mask=T.fmatrix() #(2*batch, len)
e_ids=T.imatrix() #(2*batch, len)
e_mask=T.fmatrix() #(2*batch, len)
c_heads=T.ivector() #batch
c_tails=T.ivector() #batch
l_heads=T.ivector() #batch
l_tails=T.ivector() #batch
e_heads=T.ivector() #batch
e_tails=T.ivector() #batch
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
labels=T.ivector() #batch
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = para.shape[0]
# U_p, W_p, b_p=create_GRU_para(rng, emb_size, hidden_size)
# U_p_b, W_p_b, b_p_b=create_GRU_para(rng, emb_size, hidden_size)
# GRU_p_para=[U_p, W_p, b_p, U_p_b, W_p_b, b_p_b]
#
# U_q, W_q, b_q=create_GRU_para(rng, emb_size, hidden_size)
# U_q_b, W_q_b, b_q_b=create_GRU_para(rng, emb_size, hidden_size)
# GRU_q_para=[U_q, W_q, b_q, U_q_b, W_q_b, b_q_b]
paragraph_input = embeddings[para.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input = embeddings[q.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
paragraph_reps_tensor3=paragraph_model.output_tensor #(batch, 2*hidden, paralen)
# paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U_p,W=W_p,b=b_p,Ub=U_p_b,Wb=W_p_b,bb=b_p_b)
# paragraph_reps_tensor3=paragraph_model.output_tensor_conc #(batch, 2*hidden, para_len)
fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
question_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(q_input, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
q_reps=questions_model.output_sent_rep_maxpooling #(batch, 2*hidden)
# q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U_q,W=W_q,b=b_q,Ub=U_q_b,Wb=W_q_b,bb=b_q_b)
# q_reps=q_model.output_sent_rep_conc #(batch, 2*hidden)
#interaction
batch_ids=T.arange(true_batch_size)
c_heads_reps=paragraph_reps_tensor3[batch_ids,:,c_heads] #(batch, 2*hidden)
c_tails_reps=paragraph_reps_tensor3[batch_ids,:,c_tails] #(batch, 2*hidden)
candididates_reps=T.concatenate([c_heads_reps, c_tails_reps], axis=1) #(batch, 4*hidden)
l_heads_reps=paragraph_reps_tensor3[batch_ids,:,l_heads] #(batch, 2*hidden)
l_tails_reps=paragraph_reps_tensor3[batch_ids,:,l_tails] #(batch, 2*hidden)
longs_reps=T.concatenate([l_heads_reps, l_tails_reps], axis=1) #(batch, 4*hidden)
e_heads_reps=paragraph_reps_tensor3[batch_ids,:,e_heads] #(batch, 2*hidden)
e_tails_reps=paragraph_reps_tensor3[batch_ids,:,e_tails] #(batch, 2*hidden)
extensions_reps=T.concatenate([e_heads_reps, e_tails_reps], axis=1) #(batch, 4*hidden)
#glove level average
c_input = embeddings[c_ids.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
c_sum = T.sum(c_input*c_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_C_batch = c_sum/T.sqrt(T.sum(c_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
e_sum = T.sum(e_input*e_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_E_batch = e_sum/T.sqrt(T.sum(e_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
# e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
q_sum = T.sum(q_input*q_mask.dimshuffle(0,'x',1), axis=2) #(batch, emb_size)
average_Q_batch = q_sum/T.sqrt(T.sum(q_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
# def submatrix_average(matrix, head, tail):
# return T.mean(matrix[:, head:tail+1], axis=1) #emb_size
# def submatrix_average_q(matrix, head):
# return T.mean(matrix[:, head:], axis=1) #emb_size
#
# average_E_batch, _ = theano.scan(fn=submatrix_average,
# sequences=[paragraph_input,e_heads, e_tails]) #(batch, emb_size)
# average_C_batch, _ = theano.scan(fn=submatrix_average,
# sequences=[paragraph_input,c_heads, c_tails]) #(batch, emb_size)
#
# Q_valid_len=T.cast(T.sum(q_mask, axis=1), 'int32')
#
# average_Q_batch, _ = theano.scan(fn=submatrix_average_q,
# sequences=[q_input,-Q_valid_len]) #(batch, emb_size)
#classify
HL_layer_subtask_input=T.concatenate([q_reps, extensions_reps, average_E_batch, average_Q_batch], axis=1) #(batch, 6*hidden+2*emb)
HL_layer_subtask_size= 6*hidden_size+2*emb_size#HL_layer_1_input_size+2*HL_hidden_size
HL_layer_subtask_1=HiddenLayer(rng, input=HL_layer_subtask_input, n_in=HL_layer_subtask_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_subtask_2=HiddenLayer(rng, input=HL_layer_subtask_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
U_subtask_a = create_ensemble_para(rng, 2, HL_hidden_size) # the weight matrix hidden_size*2
norm_U_subtask_a=normalize_matrix(U_subtask_a)
LR_subtask_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_subtask_para=[U_subtask_a, LR_subtask_b]
layer_LR_subtask=LogisticRegression(rng, input=HL_layer_subtask_2.output, n_in=HL_hidden_size, n_out=2, W=norm_U_subtask_a, b=LR_subtask_b) #basically it is a multiplication between weight matrix and input feature vector
HL_layer_1_input_size=14*hidden_size+3*emb_size+1
#, average_E_batch, average_C_batch, average_Q_batch
HL_layer_1_input = T.concatenate([q_reps, longs_reps, extensions_reps, candididates_reps, average_E_batch, average_C_batch, average_Q_batch, layer_LR_subtask.prop_for_posi.reshape((true_batch_size,1))], axis=1) #(batch, 14*hidden_size+3*emb_size+1)
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
LR_input=HL_layer_2.output #T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output], axis=1) #(batch, 10*hidden)
LR_input_size= HL_hidden_size#HL_layer_1_input_size+2*HL_hidden_size
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=norm_U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels)+layer_LR_subtask.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = LR_para+[embeddings]+paragraph_para+question_para+HL_layer_1.params+HL_layer_2.params+LR_subtask_para+HL_layer_subtask_1.params+HL_layer_subtask_2.params
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+0.0005*T.mean(U_a**2)
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([para, para_mask,c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], cost, updates=updates,on_unused_input='ignore')
train_model_pred = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], layer_LR.y_pred, on_unused_input='ignore')
test_model = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], [layer_LR.errors(labels),layer_LR.y_pred], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_train_batches_pred=train_size/batch_size_pred #batch_size means how many pairs
train_batch_start_pred=list(np.arange(n_train_batches_pred)*batch_size_pred)+[train_size-batch_size_pred]
n_test_batches=test_size/test_batch_size #batch_size means how many pairs
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_acc=0.0
cost_i=0.0
train_ids = range(train_size)
train_ids_pred = range(train_size)
best_test_statistic=defaultdict(int)
# best_train_statistic=defaultdict(int)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
# print train_ids[:100]
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list = train_ids[para_id:para_id+batch_size]
# print 'train_labels[train_id_list]:', train_labels[train_id_list]
cost_i+= train_model(
train_paras[train_id_list],
train_paras_mask[train_id_list],
train_c_ids[train_id_list],
train_c_masks[train_id_list],
train_e_ids[train_id_list],
train_e_masks[train_id_list],
train_c_heads[train_id_list],
train_c_tails[train_id_list],
train_l_heads[train_id_list],
train_l_tails[train_id_list],
train_e_heads[train_id_list],
train_e_tails[train_id_list],
train_questions[train_id_list],
train_questions_mask[train_id_list],
train_labels[train_id_list])
#print iter
if iter%10==0: #iter>=200 and
print 'Epoch ', epoch, 'iter '+str(iter)+'/'+str(len(train_batch_start))+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
# print 'Training Pred...'
# train_statistic=defaultdict(int)
# for para_id in train_batch_start_pred:
# train_id_list = train_ids_pred[para_id:para_id+batch_size_pred]
# gold_train_labels_list = train_labels_3c[train_id_list]
# # print 'train_id_list:', train_id_list
# # print 'train_c_heads[train_id_list]:', train_c_heads[train_id_list]
# train_preds_i= train_model_pred(
# train_paras[train_id_list],
# train_paras_mask[train_id_list],
# train_c_ids[train_id_list],
# train_c_masks[train_id_list],
# train_e_ids[train_id_list],
# train_e_masks[train_id_list],
# train_c_heads[train_id_list],
# train_c_tails[train_id_list],
# train_l_heads[train_id_list],
# train_l_tails[train_id_list],
# train_e_heads[train_id_list],
# train_e_tails[train_id_list],
# train_questions[train_id_list],
# train_questions_mask[train_id_list],
# train_labels[train_id_list])
#
# for ind, gold_label in enumerate(gold_train_labels_list):
# train_statistic[(gold_label, train_preds_i[ind])]+=1
# train_acc= (train_statistic.get((1,1),0)+train_statistic.get((0,0),0))*1.0/(train_statistic.get((1,1),0)+train_statistic.get((0,0),0)+train_statistic.get((1,0),0)+train_statistic.get((0,1),0))
#
# print '\t\tcurrnt train acc:', train_acc, ' train_statistic:', train_statistic
print 'Testing...'
error=0
test_statistic=defaultdict(int)
for test_para_id in test_batch_start:
test_id_list = range(test_para_id, test_para_id+test_batch_size)
# print 'test_id_list:',test_id_list
# print 'test_c_heads[test_id_list]', test_c_heads[test_id_list]
gold_labels_list = test_labels_3c[test_para_id:test_para_id+test_batch_size]
error_i, preds_i= test_model(
test_paras[test_id_list],
test_paras_mask[test_id_list],
test_c_ids[test_id_list],
test_c_masks[test_id_list],
test_e_ids[test_id_list],
test_e_masks[test_id_list],
test_c_heads[test_id_list],
test_c_tails[test_id_list],
test_l_heads[test_id_list],
test_l_tails[test_id_list],
test_e_heads[test_id_list],
test_e_tails[test_id_list],
test_questions[test_id_list],
test_questions_mask[test_id_list],
test_labels[test_id_list])
error+=error_i
for ind, gold_label in enumerate(gold_labels_list):
test_statistic[(gold_label, preds_i[ind])]+=1
# acc=1.0-error*1.0/len(test_batch_start)
acc= (test_statistic.get((1,1),0)+test_statistic.get((0,0),0))*1.0/(test_statistic.get((1,1),0)+test_statistic.get((0,0),0)+test_statistic.get((1,0),0)+test_statistic.get((0,1),0))
if acc> max_acc:
max_acc=acc
best_test_statistic=test_statistic
store_model_to_file(storePath+'Best_Paras_HS_v2_000_subtask_'+str(max_acc), params)
print 'Finished storing best params at:', max_acc
print 'current average acc:', acc, '\t\tmax acc:', max_acc, '\ttest_statistic:', test_statistic
print '\t\t\t\tbest statistic:', best_test_statistic
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=300, char_emb_size=20, hidden_size=300,
L2_weight=0.0001, p_len_limit=400, test_p_len_limit=100, q_len_limit=20, char_len=15, filter_size = [5,5],
char_filter_size=3, margin=2.0, max_EM=50.302743615):
test_batch_size=batch_size*10
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
word2id={}
char2id={}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id=load_squad_cnn_rank_word_train(word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size=len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, q_idlist, word2id, char2id, test_para_wordlist_list= load_squad_cnn_rank_word_dev(word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size=len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask, dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask, dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask, dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask, dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices= T.imatrix() #batch, (start, end) for each sample
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask=T.fmatrix()
char_q_mask=T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
common_input_p=embeddings[paragraph.flatten()].reshape((true_batch_size,true_p_len, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q=embeddings[questions.flatten()].reshape((true_batch_size,q_len_limit, emb_size))
char_common_input_p=char_embeddings[char_paragraph.flatten()].reshape((true_batch_size*true_p_len, char_len, char_emb_size)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q=char_embeddings[char_questions.flatten()].reshape((true_batch_size*q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape((true_batch_size*true_p_len, char_len))
char_q_masks = char_q_mask.reshape((true_batch_size*q_len_limit, char_len))
conv_W_char, conv_b_char=create_conv_para(rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size+char_emb_size, filter_size[0]))
conv_W_2, conv_b_2=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[1]))
conv_W_1_q, conv_b_1_q=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size+char_emb_size, filter_size[0]))
conv_W_2_q, conv_b_2_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[1]))
NN_para=[conv_W_1, conv_b_1,conv_W_2, conv_b_2,conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char]
input4score = squad_cnn_rank_word(rng, common_input_p, common_input_q, char_common_input_p, char_common_input_q,batch_size, p_len_limit,q_len_limit,
emb_size, char_emb_size,char_len,filter_size,char_filter_size,hidden_size,
conv_W_1, conv_b_1,conv_W_2, conv_b_2,conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q,conv_W_char,conv_b_char,
para_mask, q_mask, char_p_masks,char_q_masks)
test_input4score = squad_cnn_rank_word(rng, common_input_p, common_input_q, char_common_input_p, char_common_input_q,test_batch_size, test_p_len_limit,q_len_limit,
emb_size, char_emb_size,char_len,filter_size,char_filter_size,hidden_size,
conv_W_1, conv_b_1,conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q, conv_W_2_q, conv_b_2_q,conv_W_char,conv_b_char,
para_mask, q_mask, char_p_masks,char_q_masks) #(batch, hidden, #(batch, 2*hidden, p_len_limit))
# gram_size = 5*true_p_len-(0+1+2+3+4)
HL_1_para = create_ensemble_para(rng, hidden_size, 2*hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a=normalize_matrix(U_a)
norm_HL_1_para=normalize_matrix(HL_1_para)
norm_HL_2_para=normalize_matrix(HL_2_para)
norm_HL_3_para=normalize_matrix(HL_3_para)
norm_HL_4_para=normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(rng, hidden_size, 2*hidden_size)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(rng, 1, hidden_size)
end_norm_U_a=normalize_matrix(end_U_a)
end_norm_HL_1_para=normalize_matrix(end_HL_1_para)
end_norm_HL_2_para=normalize_matrix(end_HL_2_para)
end_norm_HL_3_para=normalize_matrix(end_HL_3_para)
end_norm_HL_4_para=normalize_matrix(end_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para, norm_U_a, batch_size,true_p_len)
span_scores=T.nnet.softmax(span_scores_matrix) #(batch, para_len)
end_span_scores_matrix = add_HLs_2_tensor3(input4score, end_norm_HL_1_para,end_norm_HL_2_para,end_norm_HL_3_para,end_norm_HL_4_para, end_norm_U_a, batch_size,true_p_len)
end_span_scores=T.nnet.softmax(end_span_scores_matrix) #(batch, para_len)
loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(batch_size), gold_indices[:,0]]))
end_loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(batch_size), gold_indices[:,1]]))
#ranking loss start
tanh_span_scores_matrix = span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, p_len_limit), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(index_matrix[T.arange(batch_size), gold_indices[:,0]], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0-new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
#ranking loss END
end_tanh_span_scores_matrix = end_span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit), dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(end_index_matrix[T.arange(batch_size), gold_indices[:,1]], 1.0)
end_prob_batch_posi = end_tanh_span_scores_matrix[end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_span_scores_matrix[(1.0-end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi, end_prob_batch_nega.shape[0], axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle('x',0), end_prob_batch_posi.shape[0], axis=0).flatten()
end_loss_rank = T.mean(T.maximum(0.0, margin-end_repeat_posi+end_repeat_nega))
loss = loss_neg_likelihood +end_loss_neg_likelihood+loss_rank+end_loss_rank
#test
test_span_scores_matrix = add_HLs_2_tensor3(test_input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para,norm_U_a, true_batch_size,true_p_len) #(batch, test_p_len)
mask_test_return=T.argmax(test_span_scores_matrix*para_mask, axis=1) #batch
end_test_span_scores_matrix = add_HLs_2_tensor3(test_input4score, end_norm_HL_1_para,end_norm_HL_2_para,end_norm_HL_3_para,end_norm_HL_4_para,end_norm_U_a, true_batch_size,true_p_len) #(batch, test_p_len)
end_mask_test_return=T.argmax(end_test_span_scores_matrix*para_mask, axis=1) #batch
params = [embeddings,char_embeddings]+NN_para+[U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para]+[end_U_a,end_HL_1_para,end_HL_2_para,end_HL_3_para,end_HL_4_para]
L2_reg =L2norm_paraList([embeddings,char_embeddings,conv_W_1,conv_W_2,conv_W_1_q, conv_W_2_q, conv_W_char,U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para])
#L2_reg = L2norm_paraList(params)
cost=loss#+L2_weight*L2_reg
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, char_paragraph, #(batch, char_len*p_len)
char_questions, char_para_mask, char_q_mask, true_p_len], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask,
char_paragraph,
char_questions,
char_para_mask,
char_q_mask,
true_p_len], [mask_test_return,end_mask_test_return], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_ids[para_id:para_id+batch_size]
cost_i+= train_model(
train_para_list[train_id_batch],
train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch],
p_len_limit)
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict={}
q_amount=0
p1=0
for test_para_id in test_batch_start:
batch_predict_ids, batch_predict_end_ids=test_model(
test_para_list[test_para_id:test_para_id+test_batch_size],
test_Q_list[test_para_id:test_para_id+test_batch_size],
test_para_mask[test_para_id:test_para_id+test_batch_size],
test_Q_mask[test_para_id:test_para_id+test_batch_size],
test_para_char_list[test_para_id:test_para_id+test_batch_size],
test_Q_char_list[test_para_id:test_para_id+test_batch_size],
test_para_char_mask[test_para_id:test_para_id+test_batch_size],
test_Q_char_mask[test_para_id:test_para_id+test_batch_size],
test_p_len_limit)
test_para_wordlist_batch=test_para_wordlist_list[test_para_id:test_para_id+test_batch_size]
# test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
# q_amount+=test_batch_size
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
q_amount+=test_batch_size
for q in range(test_batch_size): #for each question
# pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_batch[q])
start = batch_predict_ids[q]
end = batch_predict_end_ids[q]
if end < start:
start, end = end, start
pred_ans = ' '.join(test_para_wordlist_batch[q][start:end+1])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=2000,
batch_size=100,
emb_size=300,
char_emb_size=20,
hidden_size=300,
L2_weight=0.0001,
p_len_limit=400,
test_p_len_limit=100,
q_len_limit=20,
char_len=15,
filter_size=[5, 5],
char_filter_size=3,
margin=2.0,
max_EM=50.302743615):
test_batch_size = batch_size * 10
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
word2id = {}
char2id = {}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list, train_para_list, train_Q_mask, train_para_mask, train_Q_char_list, train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id = load_squad_cnn_rank_word_train(
word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size = len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask, test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, q_idlist, word2id, char2id, test_para_wordlist_list = load_squad_cnn_rank_word_dev(
word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size = len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask,
dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask,
dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask,
dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask,
dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask,
dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX, rng)
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX, rng)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices = T.imatrix() #batch, (start, end) for each sample
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask = T.fmatrix()
char_q_mask = T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
common_input_p = embeddings[paragraph.flatten()].reshape(
(true_batch_size, true_p_len,
emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q = embeddings[questions.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size))
char_common_input_p = char_embeddings[char_paragraph.flatten()].reshape(
(true_batch_size * true_p_len, char_len, char_emb_size
)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q = char_embeddings[char_questions.flatten()].reshape(
(true_batch_size * q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape(
(true_batch_size * true_p_len, char_len))
char_q_masks = char_q_mask.reshape(
(true_batch_size * q_len_limit, char_len))
conv_W_char, conv_b_char = create_conv_para(
rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1 = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_1_q, conv_b_1_q = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2_q, conv_b_2_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
NN_para = [
conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q,
conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char
]
input4score = squad_cnn_rank_word(
rng, common_input_p, common_input_q, char_common_input_p,
char_common_input_q, batch_size, p_len_limit, q_len_limit, emb_size,
char_emb_size, char_len, filter_size, char_filter_size, hidden_size,
conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q, conv_b_1_q,
conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char, para_mask, q_mask,
char_p_masks, char_q_masks) #(batch, 4*hidden, p_len_limit)
test_input4score = squad_cnn_rank_word(
rng, common_input_p, common_input_q, char_common_input_p,
char_common_input_q, test_batch_size, test_p_len_limit, q_len_limit,
emb_size, char_emb_size, char_len, filter_size, char_filter_size,
hidden_size, conv_W_1, conv_b_1, conv_W_2, conv_b_2, conv_W_1_q,
conv_b_1_q, conv_W_2_q, conv_b_2_q, conv_W_char, conv_b_char,
para_mask, q_mask, char_p_masks,
char_q_masks) #(batch, 4*hidden, p_len_limit)
# gram_size = 5*true_p_len-(0+1+2+3+4)
HL_1_para = create_ensemble_para(rng, hidden_size,
6 * hidden_size + char_emb_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a = normalize_matrix(U_a)
norm_HL_1_para = normalize_matrix(HL_1_para)
norm_HL_2_para = normalize_matrix(HL_2_para)
norm_HL_3_para = normalize_matrix(HL_3_para)
norm_HL_4_para = normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(rng, hidden_size,
6 * hidden_size + char_emb_size)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(rng, 1, hidden_size)
end_norm_U_a = normalize_matrix(end_U_a)
end_norm_HL_1_para = normalize_matrix(end_HL_1_para)
end_norm_HL_2_para = normalize_matrix(end_HL_2_para)
end_norm_HL_3_para = normalize_matrix(end_HL_3_para)
end_norm_HL_4_para = normalize_matrix(end_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,
norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_U_a,
batch_size, true_p_len)
span_scores = T.nnet.softmax(span_scores_matrix) #(batch, para_len)
end_span_scores_matrix = add_HLs_2_tensor3(input4score, end_norm_HL_1_para,
end_norm_HL_2_para,
end_norm_HL_3_para,
end_norm_HL_4_para,
end_norm_U_a, batch_size,
true_p_len)
end_span_scores = T.nnet.softmax(
end_span_scores_matrix) #(batch, para_len)
loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), gold_indices[:, 0]]))
end_loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), gold_indices[:, 1]]))
#ranking loss start
tanh_span_scores_matrix = span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(
index_matrix[T.arange(batch_size), gold_indices[:, 0]], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0 -
new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi,
prob_batch_nega.shape[0],
axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x', 0),
prob_batch_posi.shape[0],
axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin - repeat_posi + repeat_nega))
#ranking loss END
end_tanh_span_scores_matrix = end_span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(
end_index_matrix[T.arange(batch_size), gold_indices[:, 1]], 1.0)
end_prob_batch_posi = end_tanh_span_scores_matrix[
end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_span_scores_matrix[(
1.0 - end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi,
end_prob_batch_nega.shape[0],
axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle(
'x', 0),
end_prob_batch_posi.shape[0],
axis=0).flatten()
end_loss_rank = T.mean(
T.maximum(0.0, margin - end_repeat_posi + end_repeat_nega))
loss = loss_neg_likelihood + end_loss_neg_likelihood + loss_rank + end_loss_rank
#test
test_span_scores_matrix = add_HLs_2_tensor3(
test_input4score, norm_HL_1_para, norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
mask_test_return = T.argmax(test_span_scores_matrix * para_mask,
axis=1) #batch
end_test_span_scores_matrix = add_HLs_2_tensor3(
test_input4score, end_norm_HL_1_para, end_norm_HL_2_para,
end_norm_HL_3_para, end_norm_HL_4_para, end_norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
end_mask_test_return = T.argmax(end_test_span_scores_matrix * para_mask,
axis=1) #batch
params = (
[embeddings, char_embeddings] + NN_para +
[U_a, HL_1_para, HL_2_para, HL_3_para, HL_4_para] +
[end_U_a, end_HL_1_para, end_HL_2_para, end_HL_3_para, end_HL_4_para])
L2_reg = L2norm_paraList([
embeddings, char_embeddings, conv_W_1, conv_W_2, conv_W_1_q,
conv_W_2_q, conv_W_char, U_a, HL_1_para, HL_2_para, HL_3_para,
HL_4_para
])
#L2_reg = L2norm_paraList(params)
cost = loss + L2_weight * L2_reg
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function(
[
paragraph,
questions,
gold_indices,
para_mask,
q_mask,
char_paragraph, #(batch, char_len*p_len)
char_questions,
char_para_mask,
char_q_mask,
true_p_len
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, questions, para_mask, q_mask, char_paragraph,
char_questions, char_para_mask, char_q_mask, true_p_len
], [mask_test_return, end_mask_test_return],
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
# remain_test=test_size%batch_size
test_batch_start = list(numpy.arange(n_test_batches) *
test_batch_size) + [test_size - test_batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.Random(4).shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_ids[para_id:para_id + batch_size]
cost_i += train_model(
train_para_list[train_id_batch], train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch], train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch], p_len_limit)
#print iter
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict = {}
q_amount = 0
p1 = 0
for test_para_id in test_batch_start:
batch_predict_ids, batch_predict_end_ids = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size],
test_Q_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_mask[test_para_id:test_para_id +
test_batch_size],
test_para_char_list[test_para_id:test_para_id +
test_batch_size],
test_Q_char_list[test_para_id:test_para_id +
test_batch_size],
test_para_char_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_char_mask[test_para_id:test_para_id +
test_batch_size], test_p_len_limit)
test_para_wordlist_batch = test_para_wordlist_list[
test_para_id:test_para_id + test_batch_size]
# test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
# q_amount+=test_batch_size
q_ids_batch = q_idlist[test_para_id:test_para_id +
test_batch_size]
q_amount += test_batch_size
for q in range(test_batch_size): #for each question
# pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_batch[q])
start = batch_predict_ids[q]
end = batch_predict_end_ids[q]
if end < start:
start, end = end, start
pred_ans = ' '.join(
test_para_wordlist_batch[q][start:end + 1])
q_id = q_ids_batch[q]
pred_dict[q_id] = pred_ans
with codecs.open(rootPath + 'predictions.txt', 'w',
'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath + 'dev-v1.1.json',
rootPath + 'predictions.txt')
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1,
n_epochs=2000,
word_nkerns=500,
char_nkerns=100,
batch_size=1,
window_width=3,
emb_size=500,
char_emb_size=100,
hidden_size=200,
margin=0.5,
L2_weight=0.0003,
update_freq=1,
norm_threshold=5.0,
max_truncate=40,
max_char_len=40,
max_des_len=20,
max_relation_len=5,
max_Q_len=30,
train_neg_size=6,
neg_all=100,
train_size=75893,
test_size=19168,
mark='_BiasedMaxPool_lr0.1_word500_char100_noDes_ent2.0'
): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/freebase/SimpleQuestions_v2/'
triple_files = [
'annotated_fb_data_train.entitylinking.top20_succSet_asInput.txt',
'annotated_fb_data_test.entitylinking.top20_succSet_asInput.fromMo_FB5M.txt'
]
rng = numpy.random.RandomState(23455)
word2id, char2id = load_word2id_char2id(mark)
# datasets, datasets_test, length_per_example_test, vocab_size, char_size=load_test_or_valid(triple_files[0], triple_files[1], max_char_len, max_des_len, max_relation_len, max_Q_len, train_size, test_size)#max_char_len, max_des_len, max_relation_len, max_Q_len
datasets_test, length_per_example_test, word2id, char2id = load_test_or_valid(
triple_files[1], char2id, word2id, max_char_len, max_des_len,
max_relation_len, max_Q_len, test_size)
vocab_size = len(word2id)
char_size = len(char2id)
print 'vocab_size:', vocab_size, 'char_size:', char_size
# train_data=datasets
# valid_data=datasets[1]
test_data = datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
# train_pos_entity_char=train_data[0]
# train_pos_entity_des=train_data[1]
# train_relations=train_data[2]
# train_entity_char_lengths=train_data[3]
# train_entity_des_lengths=train_data[4]
# train_relation_lengths=train_data[5]
# train_mention_char_ids=train_data[6]
# train_remainQ_word_ids=train_data[7]
# train_mention_char_lens=train_data[8]
# train_remainQ_word_len=train_data[9]
# train_entity_scores=train_data[10]
test_pos_entity_char = test_data[0]
# test_pos_entity_des=test_data[1]
test_relations = test_data[2]
test_entity_char_lengths = test_data[3]
# test_entity_des_lengths=test_data[4]
test_relation_lengths = test_data[5]
test_mention_char_ids = test_data[6]
test_remainQ_word_ids = test_data[7]
test_mention_char_lens = test_data[8]
test_remainQ_word_len = test_data[9]
test_entity_scores = test_data[10]
#
# test_pos_entity_char=test_data[0] #matrix, each row for line example, all head and tail entity, iteratively: 40*2*51
# test_pos_entity_des=test_data[1] #matrix, each row for a examle: 20*2*51
# test_relations=test_data[2] #matrix, each row for a example: 5*51
# test_entity_char_lengths=test_data[3] #matrix, each row for a example: 3*2*51 (three valies for one entity)
# test_entity_des_lengths=test_data[4] #matrix, each row for a example: 3*2*51 (three values for one entity)
# test_relation_lengths=test_data[5] #matrix, each row for a example: 3*51
# test_mention_char_ids=test_data[6] #matrix, each row for a mention: 40
# test_remainQ_word_ids=test_data[7] #matrix, each row for a question: 30
# test_mention_char_lens=test_data[8] #matrix, each three values for a mention: 3
# test_remainQ_word_len=test_data[9] #matrix, each three values for a remain question: 3
# train_sizes=[len(train_pos_entity_char), len(train_pos_entity_des), len(train_relations), len(train_entity_char_lengths), len(train_entity_des_lengths),\
# len(train_relation_lengths), len(train_mention_char_ids), len(train_remainQ_word_ids), len(train_mention_char_lens), len(train_remainQ_word_len), len(train_entity_scores)]
# if sum(train_sizes)/len(train_sizes)!=train_size:
# print 'weird size:', train_sizes
# exit(0)
test_sizes=[len(test_pos_entity_char), len(test_relations), len(test_entity_char_lengths),\
len(test_relation_lengths), len(test_mention_char_ids), len(test_remainQ_word_ids), len(test_mention_char_lens), len(test_remainQ_word_len), len(test_entity_scores)]
if sum(test_sizes) / len(test_sizes) != test_size:
print 'weird size:', test_sizes
exit(0)
# n_train_batches=train_size/batch_size
# n_test_batches=test_size/batch_size
# train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
# test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_pos_entity_char=pythonList_into_theanoIntMatrix(train_pos_entity_char)
# indices_train_pos_entity_des=pythonList_into_theanoIntMatrix(train_pos_entity_des)
# indices_train_relations=pythonList_into_theanoIntMatrix(train_relations)
# indices_train_entity_char_lengths=pythonList_into_theanoIntMatrix(train_entity_char_lengths)
# indices_train_entity_des_lengths=pythonList_into_theanoIntMatrix(train_entity_des_lengths)
# indices_train_relation_lengths=pythonList_into_theanoIntMatrix(train_relation_lengths)
# indices_train_mention_char_ids=pythonList_into_theanoIntMatrix(train_mention_char_ids)
# indices_train_remainQ_word_ids=pythonList_into_theanoIntMatrix(train_remainQ_word_ids)
# indices_train_mention_char_lens=pythonList_into_theanoIntMatrix(train_mention_char_lens)
# indices_train_remainQ_word_len=pythonList_into_theanoIntMatrix(train_remainQ_word_len)
# indices_train_entity_scores=pythonList_into_theanoFloatMatrix(train_entity_scores)
# indices_test_pos_entity_char=pythonList_into_theanoIntMatrix(test_pos_entity_char)
# indices_test_pos_entity_des=pythonList_into_theanoIntMatrix(test_pos_entity_des)
# indices_test_relations=pythonList_into_theanoIntMatrix(test_relations)
# indices_test_entity_char_lengths=pythonList_into_theanoIntMatrix(test_entity_char_lengths)
# indices_test_entity_des_lengths=pythonList_into_theanoIntMatrix(test_entity_des_lengths)
# indices_test_relation_lengths=pythonList_into_theanoIntMatrix(test_relation_lengths)
# indices_test_mention_char_ids=pythonList_into_theanoIntMatrix(test_mention_char_ids)
# indices_test_remainQ_word_ids=pythonList_into_theanoIntMatrix(test_remainQ_word_ids)
# indices_test_mention_char_lens=pythonList_into_theanoIntMatrix(test_mention_char_lens)
# indices_test_remainQ_word_len=pythonList_into_theanoIntMatrix(test_remainQ_word_len)
# indices_test_entity_scores=pythonList_into_theanoIntMatrix(test_entity_scores)
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
# rand_values=load_word2vec_to_init(rand_values, rootPath+'word_emb.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# char_rand_values[0]=numpy.array(numpy.zeros(char_emb_size),dtype=theano.config.floatX)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.iscalar()
chosed_indices = T.ivector()
ent_char_ids_M = T.imatrix()
ent_lens_M = T.imatrix()
men_char_ids_M = T.imatrix()
men_lens_M = T.imatrix()
rel_word_ids_M = T.imatrix()
rel_word_lens_M = T.imatrix()
#desH_word_ids_M=T.imatrix()
#desH_word_lens_M=T.imatrix()
q_word_ids_M = T.imatrix()
q_word_lens_M = T.imatrix()
ent_scores = T.fvector()
filter_size = (emb_size, window_width)
char_filter_size = (char_emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
char_filter_shape = (char_nkerns, 1, char_filter_size[0],
char_filter_size[1])
word_filter_shape = (word_nkerns, 1, filter_size[0], filter_size[1])
char_conv_W, char_conv_b = create_conv_para(rng,
filter_shape=char_filter_shape)
q_rel_conv_W, q_rel_conv_b = create_conv_para(
rng, filter_shape=word_filter_shape)
#q_desH_conv_W, q_desH_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
params = [
char_embeddings, embeddings, char_conv_W, char_conv_b, q_rel_conv_W,
q_rel_conv_b
] #, q_desH_conv_W, q_desH_conv_b]
load_model_from_file(rootPath, params, mark)
def SimpleQ_matches_Triple(ent_char_ids_f, ent_lens_f, rel_word_ids_f,
rel_word_lens_f, men_char_ids_f, q_word_ids_f,
men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng,
input=ent_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
men_char_conv = Conv_with_input_para(rng,
input=men_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
#q_desH
#q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
#desH_conv = Conv_with_input_para(rng, input=desH_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
ent_conv_pool = Max_Pooling(rng,
input_l=ent_char_conv.output,
left_l=ent_lens_f[0],
right_l=ent_lens_f[2])
men_conv_pool = Max_Pooling(rng,
input_l=men_char_conv.output,
left_l=men_lens_f[0],
right_l=men_lens_f[2])
#q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_rel_pool = Average_Pooling_for_SimpleQA(
rng,
input_l=q_rel_conv.output,
input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2],
length_l=q_word_lens_f[1] + filter_size[1] - 1,
dim=max_Q_len + filter_size[1] - 1,
topk=2)
#q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
#desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)*0.33333+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)*0.55
# 0.0*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(SimpleQ_matches_Triple,
sequences=[
ent_char_ids_M, ent_lens_M,
rel_word_ids_M, rel_word_lens_M,
men_char_ids_M, q_word_ids_M,
men_lens_M, q_word_lens_M
])
simi_list += 0.2 * ent_scores
posi_simi = simi_list[0]
nega_simies = simi_list[1:]
loss_simi_list = T.maximum(
0.0, margin - posi_simi.reshape((1, 1)) + nega_simies)
loss_simi = T.sum(loss_simi_list)
test_model = theano.function([
ent_char_ids_M, ent_lens_M, men_char_ids_M, men_lens_M, rel_word_ids_M,
rel_word_lens_M, q_word_ids_M, q_word_lens_M, ent_scores
], [loss_simi, simi_list],
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... testing'
start_time = time.clock()
mid_time = start_time
epoch = 0
test_loss = []
succ = 0
for i in range(test_size):
#prepare data
test_ent_char_ids_M = numpy.asarray(test_pos_entity_char[i],
dtype='int32').reshape(
(length_per_example_test[i],
max_char_len))
test_ent_lens_M = numpy.asarray(test_entity_char_lengths[i],
dtype='int32').reshape(
(length_per_example_test[i], 3))
test_men_char_ids_M = numpy.asarray(test_mention_char_ids[i],
dtype='int32').reshape(
(length_per_example_test[i],
max_char_len))
test_men_lens_M = numpy.asarray(test_mention_char_lens[i],
dtype='int32').reshape(
(length_per_example_test[i], 3))
test_rel_word_ids_M = numpy.asarray(test_relations[i],
dtype='int32').reshape(
(length_per_example_test[i],
max_relation_len))
test_rel_word_lens_M = numpy.asarray(test_relation_lengths[i],
dtype='int32').reshape(
(length_per_example_test[i],
3))
#test_desH_word_ids_M =numpy.asarray( test_pos_entity_des[i], dtype='int32').reshape((length_per_example_test[i], max_des_len))
#test_desH_word_lens_M = numpy.asarray(test_entity_des_lengths[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(test_remainQ_word_ids[i],
dtype='int32').reshape(
(length_per_example_test[i],
max_Q_len))
test_q_word_lens_M = numpy.asarray(test_remainQ_word_len[i],
dtype='int32').reshape(
(length_per_example_test[i], 3))
test_ent_scores = numpy.asarray(test_entity_scores[i],
dtype=theano.config.floatX)
loss_simi_i, simi_list_i = test_model(
test_ent_char_ids_M, test_ent_lens_M, test_men_char_ids_M,
test_men_lens_M, test_rel_word_ids_M, test_rel_word_lens_M,
test_q_word_ids_M, test_q_word_lens_M, test_ent_scores)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if len(simi_list_i) == 1 or simi_list_i[0] >= max(simi_list_i[1:]):
succ += 1
if i % 1000 == 0:
print 'testing', i, '...acc:', (succ * 1.0 /
(i + 1)) * (19168 * 1.0 / 21687)
succ = succ * 100.0 / 21687
#now, check MAP and MRR
print 'accu:', succ
# store_model_to_file(rootPath, params, succ, mark)
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
def evaluate_lenet5(learning_rate=0.0001, n_epochs=2000, nkerns=[50,50], batch_size=10, window_width=3,
maxSentLength=64, emb_size=50, hidden_size=200,
margin=0.5, L2_weight=0.0006, update_freq=1, norm_threshold=5.0, max_truncate=33):# max_truncate can be 45
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SICK/';
rng = numpy.random.RandomState(23455)
# datasets, vocab_size=load_SICK_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'train.txt', rootPath+'test.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
datasets, vocab_size=load_SICK_corpus(rootPath+'vocab.txt', rootPath+'train_plus_dev.txt', rootPath+'test.txt', max_truncate,maxSentLength, entailment=True)
mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad = datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
'''
indices_train_l=T.cast(indices_train_l, 'int32')
indices_train_r=T.cast(indices_train_r, 'int32')
indices_test_l=T.cast(indices_test_l, 'int32')
indices_test_r=T.cast(indices_test_r, 'int32')
'''
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_glove_50d.txt')
# rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lvector()
right_l=T.lvector()
left_r=T.lvector()
right_r=T.lvector()
length_l=T.lvector()
length_r=T.lvector()
norm_length_l=T.dvector()
norm_length_r=T.dvector()
mts=T.dmatrix()
extra=T.dmatrix()
discri=T.dmatrix()
cost_tmp=T.dscalar()
# #GPU
# index = T.iscalar()
# x_index_l = T.imatrix('x_index_l') # now, x is the index matrix, must be integer
# x_index_r = T.imatrix('x_index_r')
# y = T.ivector('y')
# left_l=T.iscalar()
# right_l=T.iscalar()
# left_r=T.iscalar()
# right_r=T.iscalar()
# length_l=T.iscalar()
# length_r=T.iscalar()
# norm_length_l=T.fscalar()
# norm_length_r=T.fscalar()
# #mts=T.dmatrix()
# #wmf=T.dmatrix()
# cost_tmp=T.fscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = debug_print(embeddings[x_index_l.flatten()].reshape((batch_size, maxSentLength, emb_size)).transpose(0,2,1), 'layer0_l_input')
layer0_r_input = debug_print(embeddings[x_index_r.flatten()].reshape((batch_size, maxSentLength, emb_size)).transpose(0,2,1), 'layer0_r_input')
#paras:
U, W, b=create_GRU_para(rng, emb_size, nkerns[0])
layer0_para=[U, W, b]
U1, W1, b1=create_GRU_para(rng, nkerns[0], nkerns[1])
layer1_para=[U1, W1, b1]
def loop (l_left, l_right, l_matrix, r_left, r_right, r_matrix, mts_i, extra_i, norm_length_l_i, norm_length_r_i):
l_input_tensor=debug_print(Matrix_Bit_Shift(l_matrix[:,l_left:-l_right]), 'l_input_tensor')
r_input_tensor=debug_print(Matrix_Bit_Shift(r_matrix[:,r_left:-r_right]), 'r_input_tensor')
addition_l=T.sum(l_matrix[:,l_left:-l_right], axis=1)
addition_r=T.sum(r_matrix[:,r_left:-r_right], axis=1)
cosine_addition=cosine(addition_l, addition_r)
eucli_addition=1.0/(1.0+EUCLID(addition_l, addition_r))#25.2%
layer0_A1 = GRU_Batch_Tensor_Input(X=l_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A2 = GRU_Batch_Tensor_Input(X=r_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
cosine_sent=cosine(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep)
eucli_sent=1.0/(1.0+EUCLID(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep))#25.2%
attention_matrix=compute_simi_feature_matrix_with_matrix(layer0_A1.output_matrix, layer0_A2.output_matrix, layer0_A1.dim, layer0_A2.dim, maxSentLength*(maxSentLength+1)/2)
l_max_attention=T.max(attention_matrix, axis=1)
neighborsArgSorted = T.argsort(l_max_attention)
kNeighborsArg = neighborsArgSorted[:3]#only average the min 3 vectors
ll = T.sort(kNeighborsArg).flatten() # make y indices in acending lie
r_max_attention=T.max(attention_matrix, axis=0)
neighborsArgSorted_r = T.argsort(r_max_attention)
kNeighborsArg_r = neighborsArgSorted_r[:3]#only average the min 3 vectors
rr = T.sort(kNeighborsArg_r).flatten() # make y indices in acending lie
l_max_min_attention=debug_print(layer0_A1.output_matrix[:,ll], 'l_max_min_attention')
r_max_min_attention=debug_print(layer0_A2.output_matrix[:,rr], 'r_max_min_attention')
layer1_A1=GRU_Matrix_Input(X=l_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
layer1_A2=GRU_Matrix_Input(X=r_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
vec_l=debug_print(layer1_A1.output_vector_last.reshape((1, nkerns[1])), 'vec_l')
vec_r=debug_print(layer1_A2.output_vector_last.reshape((1, nkerns[1])), 'vec_r')
# sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
# aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
# norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
# sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
# aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
# norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
#
uni_cosine=cosine(vec_l, vec_r)
# aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
# uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
# '''
# linear=Linear(sum_uni_l, sum_uni_r)
# poly=Poly(sum_uni_l, sum_uni_r)
# sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
# rbf=RBF(sum_uni_l, sum_uni_r)
# gesd=GESD(sum_uni_l, sum_uni_r)
# '''
eucli_1=1.0/(1.0+EUCLID(vec_l, vec_r))#25.2%
# #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
#
len_l=norm_length_l_i.reshape((1,1))
len_r=norm_length_r_i.reshape((1,1))
#
# '''
# len_l=length_l.reshape((1,1))
# len_r=length_r.reshape((1,1))
# '''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
# layer3_input_nn=T.concatenate([vec_l, vec_r,
# cosine_addition, eucli_addition,
# # cosine_sent, eucli_sent,
# uni_cosine,eucli_1], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
output_i=T.concatenate([vec_l, vec_r,
cosine_addition, eucli_addition,
# cosine_sent, eucli_sent,
uni_cosine,eucli_1,
mts_i.reshape((1,14)),
len_l, len_r,
extra_i.reshape((1,9))], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
return output_i
layer3_input, _ = theano.scan(fn=loop,
sequences=[left_l, right_l, layer0_l_input, left_r, right_r, layer0_r_input, mts, extra, norm_length_l, norm_length_r],
outputs_info=None,#[self.h0, None],
n_steps=batch_size)
#l_left, l_right, l_matrix, r_left, r_right, r_matrix, mts_i, extra_i, norm_length_l_i, norm_length_r_i
# x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
# x_index_r = T.lmatrix('x_index_r')
# y = T.lvector('y')
# left_l=T.lvector()
# right_l=T.lvector()
# left_r=T.lvector()
# right_r=T.lvector()
# length_l=T.lvector()
# length_r=T.lvector()
# norm_length_l=T.dvector()
# norm_length_r=T.dvector()
# mts=T.dmatrix()
# extra=T.dmatrix()
# discri=T.dmatrix()
# cost_tmp=T.dscalar()
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
feature_size=2*nkerns[1]+2+2+14+2+9
layer3_input=layer3_input.reshape((batch_size, feature_size))
layer3=LogisticRegression(rng, input=layer3_input, n_in=feature_size, n_out=3)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(U** 2).sum()+(W** 2).sum()+(U1** 2).sum()+(W1** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.errors(y),layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index: index + batch_size],
right_l: testRightPad_l[index: index + batch_size],
left_r: testLeftPad_r[index: index + batch_size],
right_r: testRightPad_r[index: index + batch_size],
length_l: testLengths_l[index: index + batch_size],
length_r: testLengths_r[index: index + batch_size],
norm_length_l: normalized_test_length_l[index: index + batch_size],
norm_length_r: normalized_test_length_r[index: index + batch_size],
mts: mt_test[index: index + batch_size],
extra: extra_test[index: index + batch_size],
discri:discri_test[index: index + batch_size]
}, on_unused_input='ignore', allow_input_downcast=True)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ layer1_para+layer0_para#+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
#
# # create a list of gradients for all model parameters
# grads = T.grad(cost, params)
#
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
updates = []
grads = T.grad(cost, params)
i = theano.shared(numpy.float64(0.))
i_t = i + 1.
fix1 = 1. - (1. - b1)**i_t
fix2 = 1. - (1. - b2)**i_t
lr_t = lr * (T.sqrt(fix2) / fix1)
for p, g in zip(params, grads):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (T.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
return updates
updates=Adam(cost=cost, params=params, lr=learning_rate)
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index: index + batch_size],
right_l: trainRightPad_l[index: index + batch_size],
left_r: trainLeftPad_r[index: index + batch_size],
right_r: trainRightPad_r[index: index + batch_size],
length_l: trainLengths_l[index: index + batch_size],
length_r: trainLengths_r[index: index + batch_size],
norm_length_l: normalized_train_length_l[index: index + batch_size],
norm_length_r: normalized_train_length_r[index: index + batch_size],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
}, on_unused_input='ignore', allow_input_downcast=True)
train_model_predict = theano.function([index, cost_tmp], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index: index + batch_size],
right_l: trainRightPad_l[index: index + batch_size],
left_r: trainLeftPad_r[index: index + batch_size],
right_r: trainRightPad_r[index: index + batch_size],
length_l: trainLengths_l[index: index + batch_size],
length_r: trainLengths_r[index: index + batch_size],
norm_length_l: normalized_train_length_l[index: index + batch_size],
norm_length_r: normalized_train_length_r[index: index + batch_size],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
}, on_unused_input='ignore', allow_input_downcast=True)
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
epoch = 0
done_looping = False
acc_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
# shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
# if (batch_start+1)%1000==0:
# print batch_start+1, 'uses ', (time.time()-mid_time)/60.0, 'min'
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start, 0.0)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
for i in test_batch_start:
test_loss, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y)
test_features.append(layer3_input)
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_features=numpy.concatenate(test_features, axis=0)
test_y=numpy.concatenate(test_y, axis=0)
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
(1-test_score) * 100.))
acc_nn=1-test_score
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
#this step is risky: if the training data is too big, then this step will make the training time twice longer
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start, 0.0)
train_y.append(y)
train_features.append(layer3_input)
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
train_features=numpy.concatenate(train_features, axis=0)
train_y=numpy.concatenate(train_y, axis=0)
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results=clf.predict(test_features)
lr=linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr=lr.predict(test_features)
corr_count=0
corr_count_lr=0
test_size=len(test_y)
for i in range(test_size):
if results[i]==test_y[i]:
corr_count+=1
if results_lr[i]==test_y[i]:
corr_count_lr+=1
acc_svm=corr_count*1.0/test_size
acc_lr=corr_count_lr*1.0/test_size
if acc_svm > acc_max:
acc_max=acc_svm
best_epoch=epoch
if acc_lr > acc_max:
acc_max=acc_lr
best_epoch=epoch
if acc_nn > acc_max:
acc_max=acc_nn
best_epoch=epoch
print 'acc_nn:', acc_nn, 'acc_lr:', acc_lr, 'acc_svm:', acc_svm, ' max acc: ', acc_max , ' at epoch: ', best_epoch
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=10, test_batch_size=200, emb_size=300, hidden_size=100,
L2_weight=0.0001, para_len_limit=300, q_len_limit=30, max_EM=40.0):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
# glove_vocab=set(word2vec.keys())
train_para_list, train_Q_list, train_start_list,train_end_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train_AI2(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_start_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist, q_idlist= load_dev_or_test_AI2(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in overall_word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
# labels = T.imatrix('labels') #(batch, para_len)
start_indices= T.ivector() #batch
end_indices = T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size=paragraph.shape[0]
norm_extraF=normalize_matrix(extraF)
fwd_para=create_LSTM_para(rng, emb_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_para=create_LSTM_para(rng, emb_size, hidden_size)
paragraph_para=fwd_para.values()+ bwd_para.values()
fwd_e1=create_LSTM_para(rng, 8*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e1=create_LSTM_para(rng, 8*hidden_size, hidden_size)
paragraph_para_e1=fwd_e1.values()+ bwd_e1.values()
fwd_e11=create_LSTM_para(rng, 2*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e11=create_LSTM_para(rng, 2*hidden_size, hidden_size)
paragraph_para_e11=fwd_e11.values()+ bwd_e11.values()
fwd_e2=create_LSTM_para(rng, 2*hidden_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
bwd_e2=create_LSTM_para(rng, 2*hidden_size, hidden_size)
paragraph_para_e2=fwd_e2.values()+ bwd_e2.values()
# U_e2, W_e2, b_e2=create_GRU_para(rng, hidden_size, hidden_size)
# U_e2_b, W_e2_b, b_e2_b=create_GRU_para(rng, hidden_size, hidden_size)
# paragraph_para_e2=[U_e2, W_e2, b_e2, U_e2_b, W_e2_b, b_e2_b]
# fwd_Q=create_LSTM_para(rng, emb_size, hidden_size) #create_LSTM_para(rng, word_dim, hidden_dim)
# bwd_Q=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_Q.values()+ bwd_Q.values()
# W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
# W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a1 = create_ensemble_para(rng, 1, 10*hidden_size) # 3 extra features
U_a2 = create_ensemble_para(rng, 1, 10*hidden_size) # 3 extra features
U_a3 = create_ensemble_para(rng, 1, 6*hidden_size) # 3 extra features
# LR_b = theano.shared(value=numpy.zeros((2,),
# dtype=theano.config.floatX), # @UndefinedVariable
# name='LR_b', borrow=True)
HL_paras=[U_a1, U_a2, U_a3]
params = [embeddings]+paragraph_para+paragraph_para_e1+paragraph_para_e11+HL_paras+paragraph_para_e2
# load_model_from_file(rootPath+'Best_Paras_AI2_31.210974456', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((true_batch_size, paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
#self, X, Mask, hidden_dim, fwd_tparams, bwd_tparams
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_para, bwd_tparams= bwd_para)
para_reps=paragraph_model.output_tensor #(batch, 2*hidden, para_len)
Qs_emb = embeddings[questions.flatten()].reshape((true_batch_size, questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, fwd_tparams=fwd_para, bwd_tparams= bwd_para)
questions_reps_tensor=questions_model.output_tensor #(batch, 2*hidden ,q_len)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((true_batch_size, 1, hidden_size)) #(batch, 1, hidden)
# questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1) #(batch, para_len, hidden)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
norm_U_a3=normalize_matrix(U_a3)
def example_in_batch(para_matrix, q_matrix):
#assume both are (2*hidden, len)
repeat_para_matrix_T=T.repeat(para_matrix.T, q_matrix.shape[1], axis=0) #(para_len*q_len, 2*hidden)
repeat_q_matrix_3D = T.repeat(q_matrix.T.dimshuffle('x',0,1), para_matrix.shape[1], axis=0) #(para_len, q_len, 2*hidden)
repeat_q_matrix_T= repeat_q_matrix_3D.reshape((repeat_q_matrix_3D.shape[0]*repeat_q_matrix_3D.shape[1], repeat_q_matrix_3D.shape[2])) #(para_len*q_len, 2*hidden)
ele_mult =repeat_para_matrix_T*repeat_q_matrix_T #(#(para_len*q_len, 2*hidden))
overall_concv = T.concatenate([repeat_para_matrix_T, repeat_q_matrix_T, ele_mult], axis=1) ##(para_len*q_len, 6*hidden)
scores=T.dot(overall_concv, norm_U_a3) #(para_len*q_len,1)
interaction_matrix=scores.reshape((para_matrix.shape[1], q_matrix.shape[1])) #(para_len, q_len)
# transpose_para_matrix=para_matrix.T
# interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
# norm_interaction_matrix=T.maximum(0.0, interaction_matrix)
q_by_para = T.dot(q_matrix, norm_interaction_matrix.T)/T.sum(norm_interaction_matrix.T, axis=0).dimshuffle('x',0) #(2*hidden, para_len)
para_by_q = T.repeat(T.dot(para_matrix, T.nnet.softmax(T.max(interaction_matrix, axis=1).dimshuffle('x',0)).T), para_matrix.shape[1], axis=1)
return (q_by_para, para_by_q)
inter_return, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
batch_q_reps=inter_return[0] #(batch, 2*hidden, para_len)
batch_para_reps=inter_return[1] #(batch, 2*hidden , para_len)
#para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), all are in (batch, hidden , para_len)
ensemble_para_reps_tensor=T.concatenate([para_reps, batch_q_reps,para_reps*batch_q_reps, para_reps*batch_para_reps], axis=1) #(batch, 4*2*hidden, para_len) questions_reps.dimshuffle(0,2,1)
para_ensemble_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=ensemble_para_reps_tensor, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e1, bwd_tparams= bwd_e1)
para_reps_tensor4score=para_ensemble_model.output_tensor #(batch, 2*hidden ,para_len)
para_ensemble_model1=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=para_reps_tensor4score, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e11, bwd_tparams= bwd_e11)
para_reps_tensor4score1=para_ensemble_model1.output_tensor #(batch, 2*hidden ,para_len)
Con_G_M=T.concatenate([ensemble_para_reps_tensor, para_reps_tensor4score1], axis=1) #(batch, 10*hidden, para_len)
#score for each para word
norm_U_a=normalize_matrix(U_a1)
start_scores=T.dot(Con_G_M.dimshuffle(0,2,1), norm_U_a) #(batch, para_len, 1)
start_scores=T.nnet.softmax(start_scores.reshape((true_batch_size, paragraph.shape[1]))) #(batch, para_len)
# para_reps_tensor4score = T.concatenate([para_reps_tensor4score, start_scores.dimshuffle(0,'x',1)], axis=1)
para_ensemble_model2=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(X=para_reps_tensor4score1, Mask=para_mask, hidden_dim=hidden_size,fwd_tparams=fwd_e2, bwd_tparams= bwd_e2)
para_reps_tensor4score2=para_ensemble_model2.output_tensor #(batch, 2*hidden ,para_len)
Con_G_M2=T.concatenate([ensemble_para_reps_tensor, para_reps_tensor4score2], axis=1) #(batch, 10*hidden, para_len)
norm_U_a2=normalize_matrix(U_a2)
end_scores=T.dot(Con_G_M2.dimshuffle(0,2,1), norm_U_a2) #(batch, para_len, 1)
end_scores=T.nnet.softmax(end_scores.reshape((true_batch_size, paragraph.shape[1]))) #(batch, para_len)
#loss train
loss=-T.mean(T.log(start_scores[T.arange(true_batch_size), start_indices])+T.log(end_scores[T.arange(true_batch_size), end_indices]))
#test
co_simi_batch_matrix=T.batched_dot((para_mask*start_scores).dimshuffle(0,1,'x'), (para_mask*end_scores).dimshuffle(0,'x',1)) #(batch, para_len, para_len)
#reset lower dialgonal
cols = numpy.concatenate([numpy.array(range(i), dtype=numpy.uint) for i in xrange(para_len_limit)])
rows = numpy.concatenate([numpy.array([i]*i, dtype=numpy.uint) for i in xrange(para_len_limit)])
c = T.set_subtensor(co_simi_batch_matrix[:,rows, cols], theano.shared(numpy.zeros(para_len_limit*(para_len_limit-1)/2)))
#reset longer than 7 size
cols2 = numpy.concatenate([numpy.array(range(i+7,para_len_limit), dtype=numpy.uint) for i in xrange(para_len_limit-7)])
rows2 = numpy.concatenate([numpy.array([i]*(para_len_limit-7-i), dtype=numpy.uint) for i in xrange(para_len_limit-7)])
c2 = T.set_subtensor(c[:,rows2, cols2], theano.shared(numpy.zeros((para_len_limit-7)*(para_len_limit-6)/2)))
test_return=T.argmax(c2.reshape((true_batch_size, para_len_limit*para_len_limit)), axis=1) #batch
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,start_indices, end_indices,para_mask, q_mask, extraF], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
numpy.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_start_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_end_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# writefile=codecs.open(rootPath+'predictions.txt', 'w', 'utf-8')
# writefile.write('{')
pred_dict={}
# exact_match=0.0
# F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
numpy.asarray(test_para_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_Q_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_para_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_feature_matrixlist[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+test_batch_size]
# para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+test_batch_size]
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
# print 'q_ids_batch:', q_ids_batch
# paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
# sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
# para_len=len(test_para_wordlist_list[0])
# if para_len!=len(distribution_matrix[0]):
# print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
# exit(0)
# q_size=len(distribution_matrix)
q_amount+=test_batch_size
# print q_size
# print test_para_word_list
# Q_list_inword=test_Q_list_word[test_para_id:test_para_id+test_batch_size]
for q in range(test_batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=decode_predict_id_AI2(batch_predict_ids[q], para_len_limit, test_para_wordlist_list[q])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
# writefile.write('"'+str(q_id)+'": "'+pred_ans+'", ')
# pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], numpy.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
# q_gold_ans_set=para_gold_ansset_list[q]
# # print test_para_wordlist_list[q]
# # print Q_list_inword[q]
# # print pred_ans.encode('utf8'), q_gold_ans_set
# if pred_ans in q_gold_ans_set:
# exact_match+=1
# F1=MacroF1(pred_ans, q_gold_ans_set)
# F1_match+=F1
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
# F1_acc=F1_match/q_amount
# exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_AI2_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
# os.system('python evaluate-v1.1.py '+rootPath+'dev-v1.1.json '+rootPath+'predictions.txt')
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self, learning_rate=0.2, n_epochs=2000, nkerns=[6, 14], batch_size=10, useAllSamples=True, ktop=4, filter_size=[7,5],
L2_weight=0.00005, useEmb=0, maxSentLength=60, sentEm_length=48, window=3,
k=5, nce_seeds=2345, only_left_context=False, wait_iter=20, embedding_size=48, newd=[100, 100], train_file_style=1, from_scratch=False, stop=1e-2):
self.write_file_name_suffix='_lr'+str(learning_rate)+'_nk'+str(nkerns[0])+'&'+str(nkerns[1])+'_bs'+str(batch_size)+'_fs'+str(filter_size[0])+'&'+str(filter_size[1])\
+'_maxSL'+str(maxSentLength)+'_win'+str(window)+'_noi'+str(k)+'_wait'+str(wait_iter)+'_wdEm'+str(embedding_size)\
+'_stEm'+str(sentEm_length)+'_ts'+str(from_scratch)+'_newd'+str(newd[0])+'&'+str(newd[1])+'_trFi'+str(train_file_style)+'stop'+str(stop)
model_options = locals().copy()
print "model options", model_options
self.ini_learning_rate=learning_rate
self.n_epochs=n_epochs
self.nkerns=nkerns
self.batch_size=batch_size
self.useAllSamples=useAllSamples
self.ktop=ktop
self.filter_size=filter_size
self.L2_weight=L2_weight
self.useEmb=useEmb
self.maxSentLength=maxSentLength
self.kmax=self.maxSentLength/2+5
self.sentEm_length=sentEm_length
self.window=window
self.k=k
self.only_left_context=only_left_context
if self.only_left_context:
self.context_size=self.window
else:
self.context_size=2*self.window
self.nce_seed=nce_seeds
self.embedding_size=0
self.train_file_style=train_file_style
#we define "train_file_style" as: 0 (wiki), 11(sent_train), 12 (senti_dev), 13 (senti_test)
senti_trainfile="/mounts/data/proj/wenpeng/Dataset/StanfordSentiment/stanfordSentimentTreebank/2classes/2train.txt"
senti_devfile="/mounts/data/proj/wenpeng/Dataset/StanfordSentiment/stanfordSentimentTreebank/2classes/2dev.txt"
senti_testfile="/mounts/data/proj/wenpeng/Dataset/StanfordSentiment/stanfordSentimentTreebank/2classes/2test.txt"
wiki_path="/mounts/data/proj/wenpeng/PhraseEmbedding/enwiki-20130503-pages-articles-cleaned-tokenized"
embeddingPath='/mounts/data/proj/wenpeng/Downloads/hlbl-embeddings-original.EMBEDDING_SIZE=50.txt'
embeddingPath2='/mounts/data/proj/wenpeng/MC/src/released_embedding.txt'
root='/mounts/data/proj/wenpeng/Thang/'
if self.train_file_style !=0:
datasets, unigram, train_lengths, word_count, self.id2word=load_training_file(senti_trainfile,self.maxSentLength,self.train_file_style)
elif self.train_file_style == 0:
#datasets, unigram, train_lengths, word_count, self.id2word=load_training_file(root+'train.txt',self.maxSentLength,self.train_file_style)
datasets, unigram, train_lengths, dev_lengths, word_count, self.id2word=load_data_for_training(root+'train.txt', root+'dev_dev93.txt',self.maxSentLength)
self.datasets=datasets
self.embedding_size=embedding_size
self.vocab_size=word_count
self.rand_values_R=random_value_normal((self.vocab_size+1, self.embedding_size), theano.config.floatX, numpy.random.RandomState(1234))
self.rand_values_R[0]=numpy.array(numpy.zeros(self.embedding_size))
self.rand_values_Q=random_value_normal((self.vocab_size+1, self.embedding_size), theano.config.floatX, numpy.random.RandomState(4321))
self.rand_values_Q[0]=numpy.array(numpy.zeros(self.embedding_size))
self.from_scratch=from_scratch
if not self.from_scratch:
self.load_pretrained_embeddings()
self.embeddings_R=theano.shared(value=self.rand_values_R)
self.embeddings_Q=theano.shared(value=self.rand_values_Q)
self.unigram=unigram # we use the average of unigram as probability of new word in dev set
self.extend_unigram=numpy.append(unigram, [sum(unigram)/len(unigram)])
#print 'unigram, p_n length:', len(unigram), len(self.extend_unigram)
self.p_n=theano.shared(value=self.extend_unigram)
self.train_lengths=train_lengths
self.vali_lengths=dev_lengths
b_values = zero_value((len(unigram)+1,), dtype=theano.config.floatX)#the last bias is for new words in dev data
#print 'bias length:', len(b_values)
self.bias = theano.shared(value=b_values, name='bias')
self.wait_iter=wait_iter
self.newd=newd
self.stop=stop
def evaluate_lenet5(learning_rate=0.08, n_epochs=2000, nkerns=[50], batch_size=1000, window_width=4,
maxSentLength=64, emb_size=5, hidden_size=50,
margin=0.5, L2_weight=0.0004, update_freq=1, norm_threshold=5.0, max_truncate=40, line_no=483142):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
triple_path='/mounts/data/proj/wenpeng/Dataset/freebase/FB15k/'
rng = numpy.random.RandomState(1234)
triples, entity_size, relation_size, entity_count, relation_count=load_triples(triple_path+'freebase_mtr100_mte100-train.txt', line_no, triple_path)#vocab_size contain train, dev and test
print 'triple size:', len(triples), 'entity_size:', entity_size, 'relation_size:', relation_size#, len(entity_count), len(relation_count)
# print triples
# print entity_count
# print relation_count
# exit(0)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
# mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
# wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
entity_count=theano.shared(numpy.asarray(entity_count, dtype=theano.config.floatX), borrow=True)
entity_count=T.cast(entity_count, 'int64')
relation_count=theano.shared(numpy.asarray(relation_count, dtype=theano.config.floatX), borrow=True)
relation_count=T.cast(relation_count, 'int64')
rand_values=random_value_normal((entity_size, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
entity_E=theano.shared(value=rand_values, borrow=True)
rand_values=random_value_normal((relation_size, emb_size), theano.config.floatX, numpy.random.RandomState(4321))
relation_E=theano.shared(value=rand_values, borrow=True)
GRU_U, GRU_W, GRU_b=create_GRU_para(rng, word_dim=emb_size, hidden_dim=emb_size)
GRU_U_combine, GRU_W_combine, GRU_b_combine=create_nGRUs_para(rng, word_dim=emb_size, hidden_dim=emb_size, n=2)
#cost_tmp=0
n_batchs=line_no/batch_size
remain_triples=line_no%batch_size
if remain_triples>0:
batch_start=list(numpy.arange(n_batchs)*batch_size)+[line_no-batch_size]
else:
batch_start=list(numpy.arange(n_batchs)*batch_size)
batch_start=theano.shared(numpy.asarray(batch_start, dtype=theano.config.floatX), borrow=True)
batch_start=T.cast(batch_start, 'int64')
# allocate symbolic variables for the data
# index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
# x_index_r = T.imatrix('x_index_r')
# y = T.ivector('y')
# left_l=T.iscalar()
# right_l=T.iscalar()
# left_r=T.iscalar()
# right_r=T.iscalar()
# length_l=T.iscalar()
# length_r=T.iscalar()
# norm_length_l=T.fscalar()
# norm_length_r=T.fscalar()
# mts=T.fmatrix()
# wmf=T.fmatrix()
# cost_tmp=T.fscalar()
# #x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
# ishape = (emb_size, maxSentLength) # this is the size of MNIST images
# filter_size=(emb_size,window_width)
# #poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
zero_entity_E=T.zeros((entity_size, emb_size))
zero_relation_E=T.zeros((relation_size, emb_size))
entity_E_hat_1, relation_E_hat_1=all_batches(batch_start, batch_size, x_index_l, entity_E, relation_E, GRU_U, GRU_W, GRU_b, emb_size, zero_entity_E,zero_relation_E, entity_count, entity_size, relation_count, relation_size)
# for start in batch_start:
# batch_triple_indices=x_index_l[start:start+batch_size]
# # entity_E_hat_1, relation_E_hat_1=one_iteration_parallel(batch_triple_indices, entity_E, relation_E, GRU_U, GRU_W, GRU_b, emb_size, entity_size, relation_size, entity_count, relation_count)
# new_entity_E,new_relation_E=one_batch_parallel(batch_triple_indices, entity_E, relation_E, GRU_U, GRU_W, GRU_b, emb_size, new_entity_E,new_relation_E)
#
# entity_count=debug_print(entity_count.reshape((entity_size,1)), 'entity_count')
# relation_count=debug_print(relation_count.reshape((relation_size, 1)), 'relation_count')
# entity_E_hat_1=debug_print(new_entity_E/entity_count+1e-6, 'entity_E_hat_1') #to get rid of zero incoming info
# relation_E_hat_1=debug_print(new_relation_E/relation_count, 'relation_E_hat_1')
#
# entity_E_hat_1, relation_E_hat_1=one_iteration_parallel(x_index_l, entity_E, relation_E, GRU_U, GRU_W, GRU_b, emb_size, entity_size, relation_size, entity_count, relation_count)
#
entity_E_updated_1=GRU_Combine_2Matrix(entity_E, entity_E_hat_1, emb_size, GRU_U_combine[0], GRU_W_combine[0], GRU_b_combine[0])
relation_E_updated_1=GRU_Combine_2Matrix(relation_E, relation_E_hat_1, emb_size, GRU_U_combine[1], GRU_W_combine[1], GRU_b_combine[1])
# cost=((entity_E_hat_1-entity_E)**2).sum()+((relation_E_hat_1-relation_E)**2).sum()
cost_1=((entity_E_updated_1-entity_E)**2).sum()+((relation_E_updated_1-relation_E)**2).sum()
entity_E_hat_2, relation_E_hat_2=all_batches(batch_start, batch_size, x_index_l, entity_E_updated_1, relation_E_updated_1, GRU_U, GRU_W, GRU_b, emb_size, zero_entity_E,zero_relation_E, entity_count, entity_size, relation_count, relation_size)
# entity_E_hat_2, relation_E_hat_2=one_iteration_parallel(x_index_l, entity_E_updated_1, relation_E_updated_1, GRU_U, GRU_W, GRU_b, emb_size, entity_size, relation_size, entity_count, relation_count)
entity_E_last_2=GRU_Combine_2Matrix(entity_E_updated_1, entity_E_hat_2, emb_size, GRU_U_combine[0], GRU_W_combine[0], GRU_b_combine[0])
relation_E_last_2=GRU_Combine_2Matrix(relation_E_updated_1, relation_E_hat_2, emb_size, GRU_U_combine[1], GRU_W_combine[1], GRU_b_combine[1])
L2_loss=debug_print((entity_E** 2).sum()+(relation_E** 2).sum()\
+(GRU_U** 2).sum()+(GRU_W** 2).sum()\
+(GRU_U_combine** 2).sum()+(GRU_W_combine** 2).sum(), 'L2_reg')
cost_sys=((entity_E_last_2-entity_E_updated_1)**2).sum()+((relation_E_last_2-relation_E_updated_1)**2).sum()
cost=cost_sys+L2_weight*L2_loss
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [entity_E, relation_E, GRU_U, GRU_W, GRU_b, GRU_U_combine, GRU_W_combine, GRU_b_combine]
# params_conv = [conv_W, conv_b]
params_to_store=[GRU_U, GRU_W, GRU_b, GRU_U_combine, GRU_W_combine, GRU_b_combine]#, entity_E_last_2, relation_E_last_2]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([x_index_l], [cost_1,cost_sys, entity_E_last_2, relation_E_last_2], updates=updates,on_unused_input='ignore')
#
# train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
# givens={
# x_index_l: indices_train_l[index: index + batch_size],
# x_index_r: indices_train_r[index: index + batch_size],
# y: trainY[index: index + batch_size],
# left_l: trainLeftPad_l[index],
# right_l: trainRightPad_l[index],
# left_r: trainLeftPad_r[index],
# right_r: trainRightPad_r[index],
# length_l: trainLengths_l[index],
# length_r: trainLengths_r[index],
# norm_length_l: normalized_train_length_l[index],
# norm_length_r: normalized_train_length_r[index],
# mts: mt_train[index: index + batch_size],
# wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
# validation_frequency = min(n_train_batches/5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_1, cost_l, entity_E_store, relation_E_store= train_model(triples)
#print 'layer3_input', layer3_input
print 'epoch:', epoch, 'cost:', cost_1, cost_l
# if patience <= iter:
# done_looping = True
# break
#after each epoch, increase the batch_size
# exit(0)
#store the paras after epoch 15
# if epoch ==22:
entity_E_store=theano.shared(numpy.asarray(entity_E_store, dtype=theano.config.floatX), borrow=True)
relation_E_store=theano.shared(numpy.asarray(relation_E_store, dtype=theano.config.floatX), borrow=True)
params_to_store=params_to_store+[entity_E_store, relation_E_store]
store_model_to_file(triple_path+'Best_Paras', params_to_store)
print 'Finished storing best params'
# exit(0)
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
word_nkerns=50,
char_nkerns=4,
batch_size=1,
window_width=[2, 5],
emb_size=50,
char_emb_size=4,
hidden_size=200,
margin=0.5,
L2_weight=0.0003,
Div_reg=0.03,
update_freq=1,
norm_threshold=5.0,
max_truncate=40,
max_char_len=40,
max_des_len=20,
max_relation_len=5,
max_Q_len=30,
train_neg_size=21,
neg_all=100,
train_size=200,
test_size=200,
mark='_forfun'): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/freebase/SimpleQuestions_v2/'
triple_files = [
'annotated_fb_data_train.entitylinking.top20_succSet_asInput.txt',
'annotated_fb_data_test.entitylinking.top20_succSet_asInput.txt'
]
rng = numpy.random.RandomState(23455)
datasets, datasets_test, length_per_example_test, vocab_size, char_size = load_train(
triple_files[0], triple_files[1], max_char_len, max_des_len,
max_relation_len, max_Q_len, train_size, test_size,
mark) #max_char_len, max_des_len, max_relation_len, max_Q_len
print 'vocab_size:', vocab_size, 'char_size:', char_size
train_data = datasets
# valid_data=datasets[1]
test_data = datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
train_pos_entity_char = train_data[0]
train_pos_entity_des = train_data[1]
train_relations = train_data[2]
train_entity_char_lengths = train_data[3]
train_entity_des_lengths = train_data[4]
train_relation_lengths = train_data[5]
train_mention_char_ids = train_data[6]
train_remainQ_word_ids = train_data[7]
train_mention_char_lens = train_data[8]
train_remainQ_word_len = train_data[9]
train_entity_scores = train_data[10]
test_pos_entity_char = test_data[0]
test_pos_entity_des = test_data[1]
test_relations = test_data[2]
test_entity_char_lengths = test_data[3]
test_entity_des_lengths = test_data[4]
test_relation_lengths = test_data[5]
test_mention_char_ids = test_data[6]
test_remainQ_word_ids = test_data[7]
test_mention_char_lens = test_data[8]
test_remainQ_word_len = test_data[9]
test_entity_scores = test_data[10]
#
# test_pos_entity_char=test_data[0] #matrix, each row for line example, all head and tail entity, iteratively: 40*2*51
# test_pos_entity_des=test_data[1] #matrix, each row for a examle: 20*2*51
# test_relations=test_data[2] #matrix, each row for a example: 5*51
# test_entity_char_lengths=test_data[3] #matrix, each row for a example: 3*2*51 (three valies for one entity)
# test_entity_des_lengths=test_data[4] #matrix, each row for a example: 3*2*51 (three values for one entity)
# test_relation_lengths=test_data[5] #matrix, each row for a example: 3*51
# test_mention_char_ids=test_data[6] #matrix, each row for a mention: 40
# test_remainQ_word_ids=test_data[7] #matrix, each row for a question: 30
# test_mention_char_lens=test_data[8] #matrix, each three values for a mention: 3
# test_remainQ_word_len=test_data[9] #matrix, each three values for a remain question: 3
train_sizes=[len(train_pos_entity_char), len(train_pos_entity_des), len(train_relations), len(train_entity_char_lengths), len(train_entity_des_lengths),\
len(train_relation_lengths), len(train_mention_char_ids), len(train_remainQ_word_ids), len(train_mention_char_lens), len(train_remainQ_word_len), len(train_entity_scores)]
if sum(train_sizes) / len(train_sizes) != train_size:
print 'weird size:', train_sizes
exit(0)
test_sizes=[len(test_pos_entity_char), len(test_pos_entity_des), len(test_relations), len(test_entity_char_lengths), len(test_entity_des_lengths),\
len(test_relation_lengths), len(test_mention_char_ids), len(test_remainQ_word_ids), len(test_mention_char_lens), len(test_remainQ_word_len), len(test_entity_scores)]
if sum(test_sizes) / len(test_sizes) != test_size:
print 'weird size:', test_sizes
exit(0)
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_pos_entity_char = pythonList_into_theanoIntMatrix(
train_pos_entity_char)
indices_train_pos_entity_des = pythonList_into_theanoIntMatrix(
train_pos_entity_des)
indices_train_relations = pythonList_into_theanoIntMatrix(train_relations)
indices_train_entity_char_lengths = pythonList_into_theanoIntMatrix(
train_entity_char_lengths)
indices_train_entity_des_lengths = pythonList_into_theanoIntMatrix(
train_entity_des_lengths)
indices_train_relation_lengths = pythonList_into_theanoIntMatrix(
train_relation_lengths)
indices_train_mention_char_ids = pythonList_into_theanoIntMatrix(
train_mention_char_ids)
indices_train_remainQ_word_ids = pythonList_into_theanoIntMatrix(
train_remainQ_word_ids)
indices_train_mention_char_lens = pythonList_into_theanoIntMatrix(
train_mention_char_lens)
indices_train_remainQ_word_len = pythonList_into_theanoIntMatrix(
train_remainQ_word_len)
indices_train_entity_scores = pythonList_into_theanoFloatMatrix(
train_entity_scores)
# indices_test_pos_entity_char=pythonList_into_theanoIntMatrix(test_pos_entity_char)
# indices_test_pos_entity_des=pythonList_into_theanoIntMatrix(test_pos_entity_des)
# indices_test_relations=pythonList_into_theanoIntMatrix(test_relations)
# indices_test_entity_char_lengths=pythonList_into_theanoIntMatrix(test_entity_char_lengths)
# indices_test_entity_des_lengths=pythonList_into_theanoIntMatrix(test_entity_des_lengths)
# indices_test_relation_lengths=pythonList_into_theanoIntMatrix(test_relation_lengths)
# indices_test_mention_char_ids=pythonList_into_theanoIntMatrix(test_mention_char_ids)
# indices_test_remainQ_word_ids=pythonList_into_theanoIntMatrix(test_remainQ_word_ids)
# indices_test_mention_char_lens=pythonList_into_theanoIntMatrix(test_mention_char_lens)
# indices_test_remainQ_word_len=pythonList_into_theanoIntMatrix(test_remainQ_word_len)
# indices_test_entity_scores=pythonList_into_theanoIntMatrix(test_entity_scores)
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'word_emb' + mark + '.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
char_rand_values[0] = numpy.array(numpy.zeros(char_emb_size),
dtype=theano.config.floatX)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.lscalar()
chosed_indices = T.lvector()
ent_char_ids_M = T.lmatrix()
ent_lens_M = T.lmatrix()
men_char_ids_M = T.lmatrix()
men_lens_M = T.lmatrix()
rel_word_ids_M = T.lmatrix()
rel_word_lens_M = T.lmatrix()
desH_word_ids_M = T.lmatrix()
desH_word_lens_M = T.lmatrix()
# desT_word_ids_M=T.lmatrix()
# desT_word_lens_M=T.lmatrix()
q_word_ids_M = T.lmatrix()
q_word_lens_M = T.lmatrix()
ent_scores = T.dvector()
#max_char_len, max_des_len, max_relation_len, max_Q_len
# ent_men_ishape = (char_emb_size, max_char_len) # this is the size of MNIST images
# rel_ishape=(emb_size, max_relation_len)
# des_ishape=(emb_size, max_des_len)
# q_ishape=(emb_size, max_Q_len)
filter_size = (emb_size, window_width[0])
char_filter_size = (char_emb_size, window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
char_filter_shape = (char_nkerns, 1, char_filter_size[0],
char_filter_size[1])
word_filter_shape = (word_nkerns, 1, filter_size[0], filter_size[1])
char_conv_W, char_conv_b = create_conv_para(rng,
filter_shape=char_filter_shape)
q_rel_conv_W, q_rel_conv_b = create_conv_para(
rng, filter_shape=word_filter_shape)
q_desH_conv_W, q_desH_conv_b = create_conv_para(
rng, filter_shape=word_filter_shape)
params = [
char_embeddings, embeddings, char_conv_W, char_conv_b, q_rel_conv_W,
q_rel_conv_b, q_desH_conv_W, q_desH_conv_b
]
char_conv_W_into_matrix = char_conv_W.reshape(
(char_conv_W.shape[0], char_conv_W.shape[2] * char_conv_W.shape[3]))
q_rel_conv_W_into_matrix = q_rel_conv_W.reshape(
(q_rel_conv_W.shape[0], q_rel_conv_W.shape[2] * q_rel_conv_W.shape[3]))
q_desH_conv_W_into_matrix = q_desH_conv_W.reshape(
(q_desH_conv_W.shape[0],
q_desH_conv_W.shape[2] * q_desH_conv_W.shape[3]))
# load_model_from_file(rootPath, params, '')
def SimpleQ_matches_Triple(ent_char_ids_f, ent_lens_f, rel_word_ids_f,
rel_word_lens_f, desH_word_ids_f,
desH_word_lens_f, men_char_ids_f, q_word_ids_f,
men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape(
(batch_size, max_des_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng,
input=ent_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
men_char_conv = Conv_with_input_para(rng,
input=men_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
#q_desH
q_desH_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1,
emb_size, max_Q_len),
filter_shape=word_filter_shape,
W=q_desH_conv_W,
b=q_desH_conv_b)
desH_conv = Conv_with_input_para(rng,
input=desH_word_input,
image_shape=(batch_size, 1, emb_size,
max_des_len),
filter_shape=word_filter_shape,
W=q_desH_conv_W,
b=q_desH_conv_b)
# #q_desT
# q_desT_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# desT_conv = Conv_with_input_para(rng, input=desT_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# ent_char_output=debug_print(ent_char_conv.output, 'ent_char.output')
# men_char_output=debug_print(men_char_conv.output, 'men_char.output')
ent_conv_pool = Max_Pooling(rng,
input_l=ent_char_conv.output,
left_l=ent_lens_f[0],
right_l=ent_lens_f[2])
men_conv_pool = Max_Pooling(rng,
input_l=men_char_conv.output,
left_l=men_lens_f[0],
right_l=men_lens_f[2])
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_rel_pool = Average_Pooling_for_SimpleQA(
rng,
input_l=q_rel_conv.output,
input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2],
length_l=q_word_lens_f[1] + filter_size[1] - 1,
dim=max_Q_len + filter_size[1] - 1,
topk=2)
q_desH_pool = Max_Pooling(rng,
input_l=q_desH_conv.output,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2])
desH_conv_pool = Max_Pooling(rng,
input_l=desH_conv.output,
left_l=desH_word_lens_f[0],
right_l=desH_word_lens_f[2])
# q_desT_pool=Max_Pooling(rng, input_l=q_desT_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
# desT_conv_pool=Max_Pooling(rng, input_l=desT_conv.output, left_l=desT_word_lens_f[0], right_l=desT_word_lens_f[2])
overall_simi=(cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)+\
cosine(q_rel_pool.topk_max_pooling, rel_conv_pool.output_maxpooling)+\
0.1*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling))/3.0
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(SimpleQ_matches_Triple,
sequences=[
ent_char_ids_M, ent_lens_M,
rel_word_ids_M, rel_word_lens_M,
desH_word_ids_M, desH_word_lens_M,
men_char_ids_M, q_word_ids_M,
men_lens_M, q_word_lens_M
])
simi_list += 0.5 * ent_scores
posi_simi = simi_list[0]
nega_simies = simi_list[1:]
loss_simi_list = T.maximum(
0.0, margin - posi_simi.reshape((1, 1)) + nega_simies)
loss_simi = T.mean(loss_simi_list)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(char_embeddings**2).sum() + (embeddings**2).sum() +
(char_conv_W**2).sum() + (q_rel_conv_W**2).sum() +
(q_desH_conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg = Diversify_Reg(char_conv_W_into_matrix) + Diversify_Reg(
q_rel_conv_W_into_matrix) + Diversify_Reg(q_desH_conv_W_into_matrix)
cost = loss_simi + L2_weight * L2_reg + Div_reg * diversify_reg
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([
ent_char_ids_M, ent_lens_M, men_char_ids_M, men_lens_M, rel_word_ids_M,
rel_word_lens_M, desH_word_ids_M, desH_word_lens_M, q_word_ids_M,
q_word_lens_M, ent_scores
], [loss_simi, simi_list],
on_unused_input='ignore')
# givens={
# ent_char_ids_M : test_pos_entity_char[index].reshape((length_per_example_test[index], max_char_len)),
# ent_lens_M : test_entity_char_lengths[index].reshape((length_per_example_test[index], 3)),
# men_char_ids_M : test_mention_char_ids[index].reshape((length_per_example_test[index], max_char_len)),
# men_lens_M : test_mention_char_lens[index].reshape((length_per_example_test[index], 3)),
# rel_word_ids_M : test_relations[index].reshape((length_per_example_test[index], max_relation_len)),
# rel_word_lens_M : test_relation_lengths[index].reshape((length_per_example_test[index], 3)),
# desH_word_ids_M : test_pos_entity_des[index].reshape((length_per_example_test[index], max_des_len)),
# desH_word_lens_M : test_entity_des_lengths[index].reshape((length_per_example_test[index], 3)),
# # desT_word_ids_M : indices_train_pos_entity_des[index].reshape(((neg_all)*2, max_des_len))[1::2],
# # desT_word_lens_M : indices_train_entity_des_lengths[index].reshape(((neg_all)*2, 3))[1::2],
# q_word_ids_M : test_remainQ_word_ids[index].reshape((length_per_example_test[index], max_Q_len)),
# q_word_lens_M : test_remainQ_word_len[index].reshape((length_per_example_test[index], 3)),
# ent_scores : test_entity_scores[index]},
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
#+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
# updates.append((acc_i, acc))
if param_i == embeddings:
updates.append(
(param_i,
T.set_subtensor(
(param_i -
learning_rate * grad_i / T.sqrt(acc + 1e-10))[0],
theano.shared(numpy.zeros(emb_size))))) #Ada
elif param_i == char_embeddings:
updates.append(
(param_i,
T.set_subtensor(
(param_i -
learning_rate * grad_i / T.sqrt(acc + 1e-10))[0],
theano.shared(numpy.zeros(char_emb_size))))) #AdaGrad
else:
updates.append(
(param_i, param_i -
learning_rate * grad_i / T.sqrt(acc + 1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, chosed_indices],
[loss_simi, cost],
updates=updates,
givens={
ent_char_ids_M:
indices_train_pos_entity_char[index].reshape(
(neg_all, max_char_len))[chosed_indices].reshape(
(train_neg_size, max_char_len)),
ent_lens_M:
indices_train_entity_char_lengths[index].reshape(
(neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
men_char_ids_M:
indices_train_mention_char_ids[index].reshape(
(neg_all, max_char_len))[chosed_indices].reshape(
(train_neg_size, max_char_len)),
men_lens_M:
indices_train_mention_char_lens[index].reshape(
(neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
rel_word_ids_M:
indices_train_relations[index].reshape(
(neg_all, max_relation_len))[chosed_indices].reshape(
(train_neg_size, max_relation_len)),
rel_word_lens_M:
indices_train_relation_lengths[index].reshape(
(neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
desH_word_ids_M:
indices_train_pos_entity_des[index].reshape(
(neg_all, max_des_len))[chosed_indices].reshape(
(train_neg_size, max_des_len)),
desH_word_lens_M:
indices_train_entity_des_lengths[index].reshape(
(neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
# desT_word_ids_M : indices_train_pos_entity_des[index].reshape(((neg_all)*2, max_des_len))[1::2],
# desT_word_lens_M : indices_train_entity_des_lengths[index].reshape(((neg_all)*2, 3))[1::2],
q_word_ids_M:
indices_train_remainQ_word_ids[index].reshape(
(neg_all, max_Q_len))[chosed_indices].reshape(
(train_neg_size, max_Q_len)),
q_word_lens_M:
indices_train_remainQ_word_len[index].reshape(
(neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
ent_scores:
indices_train_entity_scores[index][chosed_indices]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
best_test_accu = 0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#print batch_start
sample_indices = [0] + random.sample(range(1, neg_all),
train_neg_size - 1)
loss_simi_i, cost_i = train_model(batch_start, sample_indices)
# if batch_start%1==0:
# print batch_start, '\t loss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
# store_model_to_file(rootPath, params)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + '\tloss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
#if iter ==1:
# exit(0)
#
if iter % n_train_batches == 0:
test_loss = []
succ = 0
for i in range(test_size):
# print 'testing', i, '...'
#prepare data
test_ent_char_ids_M = numpy.asarray(
test_pos_entity_char[i], dtype='int64').reshape(
(length_per_example_test[i], max_char_len))
test_ent_lens_M = numpy.asarray(
test_entity_char_lengths[i], dtype='int64').reshape(
(length_per_example_test[i], 3))
test_men_char_ids_M = numpy.asarray(
test_mention_char_ids[i], dtype='int64').reshape(
(length_per_example_test[i], max_char_len))
test_men_lens_M = numpy.asarray(
test_mention_char_lens[i], dtype='int64').reshape(
(length_per_example_test[i], 3))
test_rel_word_ids_M = numpy.asarray(
test_relations[i], dtype='int64').reshape(
(length_per_example_test[i], max_relation_len))
test_rel_word_lens_M = numpy.asarray(
test_relation_lengths[i], dtype='int64').reshape(
(length_per_example_test[i], 3))
test_desH_word_ids_M = numpy.asarray(
test_pos_entity_des[i], dtype='int64').reshape(
(length_per_example_test[i], max_des_len))
test_desH_word_lens_M = numpy.asarray(
test_entity_des_lengths[i], dtype='int64').reshape(
(length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(
test_remainQ_word_ids[i], dtype='int64').reshape(
(length_per_example_test[i], max_Q_len))
test_q_word_lens_M = numpy.asarray(
test_remainQ_word_len[i], dtype='int64').reshape(
(length_per_example_test[i], 3))
test_ent_scores = numpy.asarray(test_entity_scores[i],
dtype=theano.config.floatX)
loss_simi_i, simi_list_i = test_model(
test_ent_char_ids_M, test_ent_lens_M,
test_men_char_ids_M, test_men_lens_M,
test_rel_word_ids_M, test_rel_word_lens_M,
test_desH_word_ids_M, test_desH_word_lens_M,
test_q_word_ids_M, test_q_word_lens_M, test_ent_scores)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if simi_list_i[0] >= max(simi_list_i[1:]):
succ += 1
# print 'testing', i, '...acc:', succ*1.0/(i+1)
succ = succ * 1.0 / test_size
#now, check MAP and MRR
print((
'\t\t\t\t\t\tepoch %i, minibatch %i/%i, test accu of best '
'model %f') %
(epoch, minibatch_index, n_train_batches, succ))
if best_test_accu < succ:
best_test_accu = succ
store_model_to_file(rootPath, params, mark)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=3,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59,
margin=1.0,
decay=0.95):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus_DQAAAA(
rootPath + 'vocab_DQAAAA.txt',
rootPath + 'mc500.train.tsv_standardlized.txt_DQAAAA.txt',
rootPath + 'mc500.test.tsv_standardlized.txt_DQAAAA.txt', max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
[
train_data_D, train_data_Q, train_data_A1, train_data_A2,
train_data_A3, train_data_A4, train_Label, train_Length_D,
train_Length_D_s, train_Length_Q, train_Length_A1, train_Length_A2,
train_Length_A3, train_Length_A4, train_leftPad_D, train_leftPad_D_s,
train_leftPad_Q, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3,
train_leftPad_A4, train_rightPad_D, train_rightPad_D_s,
train_rightPad_Q, train_rightPad_A1, train_rightPad_A2,
train_rightPad_A3, train_rightPad_A4
] = train_data
[
test_data_D, test_data_Q, test_data_A1, test_data_A2, test_data_A3,
test_data_A4, test_Label, test_Length_D, test_Length_D_s,
test_Length_Q, test_Length_A1, test_Length_A2, test_Length_A3,
test_Length_A4, test_leftPad_D, test_leftPad_D_s, test_leftPad_Q,
test_leftPad_A1, test_leftPad_A2, test_leftPad_A3, test_leftPad_A4,
test_rightPad_D, test_rightPad_D_s, test_rightPad_Q, test_rightPad_A1,
test_rightPad_A2, test_rightPad_A3, test_rightPad_A4
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(
rand_values, rootPath + 'vocab_DQAAAA_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A1 = T.lvector()
index_A2 = T.lvector()
index_A3 = T.lvector()
index_A4 = T.lvector()
# y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
len_Q = T.lscalar()
len_A1 = T.lscalar()
len_A2 = T.lscalar()
len_A3 = T.lscalar()
len_A4 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_Q = T.lscalar()
left_A1 = T.lscalar()
left_A2 = T.lscalar()
left_A3 = T.lscalar()
left_A4 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_Q = T.lscalar()
right_A1 = T.lscalar()
right_A2 = T.lscalar()
right_A3 = T.lscalar()
right_A4 = T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = debug_print(embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength, emb_size)).transpose(0, 2, 1),
'layer0_D_input') #.dimshuffle(0, 'x', 1, 2)
layer0_Q_input = debug_print(embeddings[index_Q.flatten()].reshape(
(maxSentLength, emb_size)).transpose(),
'layer0_Q_input') #.dimshuffle(0, 'x', 1, 2)
layer0_A1_input = debug_print(embeddings[index_A1.flatten()].reshape(
(maxSentLength,
emb_size)).transpose(), 'layer0_A1_input') #.dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape(
(maxSentLength, emb_size)).transpose() #.dimshuffle(0, 'x', 1, 2)
U, W, b, Ub, Wb, bb = create_Bi_GRU_para(rng, emb_size, nkerns[0])
layer0_para = [U, W, b, Ub, Wb, bb]
# conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
# layer2_para=[conv2_W, conv2_b]
# high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1])
# highW_para=[high_W, high_b]
#load_model(params)
layer0_D = Bi_GRU_Tensor3_Input(T=layer0_D_input[left_D:-right_D, :, :],
lefts=left_D_s[left_D:-right_D],
rights=right_D_s[left_D:-right_D],
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
Ub=Ub,
Wb=Wb,
bb=bb)
layer0_Q = Bi_GRU_Matrix_Input(X=layer0_Q_input[:, left_Q:-right_Q],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A1 = Bi_GRU_Matrix_Input(X=layer0_A1_input[:, left_A1:-right_A1],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A2 = Bi_GRU_Matrix_Input(X=layer0_A2_input[:, left_A2:-right_A2],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A3 = Bi_GRU_Matrix_Input(X=layer0_A3_input[:, left_A3:-right_A3],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_A4 = Bi_GRU_Matrix_Input(X=layer0_A4_input[:, left_A4:-right_A4],
word_dim=emb_size,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
U_b=Ub,
W_b=Wb,
b_b=bb,
bptt_truncate=-1)
layer0_D_output = debug_print(layer0_D.output,
'layer0_D.output') # hidden*2
layer0_Q_output = debug_print(layer0_Q.output_vector_last,
'layer0_Q.output') # hidden*4
layer0_A1_output = debug_print(layer0_A1.output_vector_last,
'layer0_A1.output')
layer0_A2_output = debug_print(layer0_A2.output_vector_last,
'layer0_A2.output')
layer0_A3_output = debug_print(layer0_A3.output_vector_last,
'layer0_A3.output')
layer0_A4_output = debug_print(layer0_A4.output_vector_last,
'layer0_A4.output')
#before reasoning, do a GRU for doc: d
U_d, W_d, b_d, U_db, W_db, b_db = create_Bi_GRU_para(
rng, nkerns[0] * 2, nkerns[0] * 2)
layer_d_para = [U_d, W_d, b_d, U_db, W_db, b_db]
layer_D_GRU = Bi_GRU_Matrix_Input(X=layer0_D_output,
word_dim=nkerns[0] * 2,
hidden_dim=nkerns[0] * 2,
U=U_d,
W=W_d,
b=b_d,
U_b=U_db,
W_b=W_db,
b_b=b_db,
bptt_truncate=-1)
#Reasoning Layer 1
repeat_Q = debug_print(
T.repeat(layer0_Q_output.reshape((layer0_Q_output.shape[0], 1)),
maxDocLength,
axis=1)[:, :layer_D_GRU.output_matrix.shape[1]], 'repeat_Q')
input_DNN = debug_print(
T.concatenate([layer_D_GRU.output_matrix, repeat_Q],
axis=0).transpose(),
'input_DNN') #each row is an example
output_DNN1 = HiddenLayer(rng,
input=input_DNN,
n_in=nkerns[0] * 8,
n_out=nkerns[0])
attention_W = create_ensemble_para(rng, nkerns[0], 1)
attention_weights = T.nnet.softmax(
T.dot(attention_W, output_DNN1.output.transpose()))
repeat_attentions = T.repeat(attention_weights,
layer_D_GRU.output_matrix.shape[0],
axis=0)
doc_r = T.sum(layer_D_GRU.output_matrix * repeat_attentions, axis=1)
combine_DQ = T.concatenate([doc_r, layer0_Q_output],
axis=0) # dim: hidden*6
output_DNN2 = HiddenLayer(rng,
input=combine_DQ,
n_in=nkerns[0] * 8,
n_out=nkerns[0] * 4)
# DNN_out=debug_print(output_DNN2.output.transpose(), 'DNN_out')
# U_p, W_p, b_p=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para=[U_p, W_p, b_p]
# pooling=GRU_Matrix_Input(X=DNN_out, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p,W=W_p,b=b_p,bptt_truncate=-1)
# translated_Q1=debug_print(pooling.output_vector_max, 'translated_Q1')
#
#
# #before reasoning, do a GRU for doc: d2
# U_d2, W_d2, b_d2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_d2_para=[U_d2, W_d2, b_d2]
# layer_D2_GRU = GRU_Matrix_Input(X=layer_D_GRU.output_matrix, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_d2,W=W_d2,b=b_d2,bptt_truncate=-1)
# #Reasoning Layer 2
# repeat_Q1=debug_print(T.repeat(translated_Q1.reshape((translated_Q1.shape[0],1)), maxDocLength, axis=1)[:,:layer_D2_GRU.output_matrix.shape[1]], 'repeat_Q1')
# input_DNN2=debug_print(T.concatenate([layer_D2_GRU.output_matrix,repeat_Q1], axis=0).transpose(), 'input_DNN2')#each row is an example
# output_DNN3=HiddenLayer(rng, input=input_DNN2, n_in=nkerns[0]*2, n_out=nkerns[0])
# output_DNN4=HiddenLayer(rng, input=output_DNN3.output, n_in=nkerns[0], n_out=nkerns[0])
#
# DNN_out2=debug_print(output_DNN4.output.transpose(), 'DNN_out2')
# U_p2, W_p2, b_p2=create_GRU_para(rng, nkerns[0], nkerns[0])
# layer_pooling_para2=[U_p2, W_p2, b_p2]
# pooling2=GRU_Matrix_Input(X=DNN_out2, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p2,W=W_p2,b=b_p2,bptt_truncate=-1)
translated_Q2 = debug_print(output_DNN2.output, 'translated_Q2')
QA1 = T.concatenate([translated_Q2, layer0_A1_output],
axis=0) #dim: hidden*5
QA2 = T.concatenate([translated_Q2, layer0_A2_output], axis=0)
QA3 = T.concatenate([translated_Q2, layer0_A3_output], axis=0)
QA4 = T.concatenate([translated_Q2, layer0_A4_output], axis=0)
W_HL, b_HL = create_HiddenLayer_para(rng, n_in=nkerns[0] * 8, n_out=1)
match_params = [W_HL, b_HL]
QA1_match = HiddenLayer(rng,
input=QA1,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA2_match = HiddenLayer(rng,
input=QA2,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA3_match = HiddenLayer(rng,
input=QA3,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
QA4_match = HiddenLayer(rng,
input=QA4,
n_in=nkerns[0] * 8,
n_out=1,
W=W_HL,
b=b_HL)
# simi_overall_level1=debug_print(cosine(translated_Q2, layer0_A1_output), 'simi_overall_level1')
# simi_overall_level2=debug_print(cosine(translated_Q2, layer0_A2_output), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(translated_Q2, layer0_A3_output), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(translated_Q2, layer0_A4_output), 'simi_overall_level4')
simi_overall_level1 = debug_print(QA1_match.output[0],
'simi_overall_level1')
simi_overall_level2 = debug_print(QA2_match.output[0],
'simi_overall_level2')
simi_overall_level3 = debug_print(QA3_match.output[0],
'simi_overall_level3')
simi_overall_level4 = debug_print(QA4_match.output[0],
'simi_overall_level4')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
#only use overall_simi
cost = T.maximum(
0.0, margin + simi_overall_level2 - simi_overall_level1) + T.maximum(
0.0,
margin + simi_overall_level3 - simi_overall_level1) + T.maximum(
0.0, margin + simi_overall_level4 - simi_overall_level1)
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
posi_simi = simi_overall_level1
nega_simi = T.max(
[simi_overall_level2, simi_overall_level3, simi_overall_level4])
# #use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_1
# nega_simi=T.max([simi_2, simi_3, simi_4])
L2_reg = debug_print(
(U**2).sum() + (W**2).sum() + (Ub**2).sum() + (Wb**2).sum() +
(output_DNN1.W**2).sum() + (output_DNN2.W**2).sum() + (U_d**2).sum() +
(W_d**2).sum() + (U_db**2).sum() + (W_db**2).sum() + (W_HL**2).sum() +
(attention_W**2).sum(), 'L2_reg'
) #+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost = debug_print(cost + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
},
on_unused_input='ignore')
params = layer0_para + output_DNN1.params + output_DNN2.params + match_params + layer_d_para + [
attention_W
]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = decay*acc_i + (1-decay)*T.sqr(grad_i) #rmsprop
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-6)))
# updates.append((acc_i, acc))
def AdaDelta_updates(parameters, gradients, rho, eps):
# create variables to store intermediate updates
gradients_sq = [
theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters
]
deltas_sq = [
theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters
]
# calculates the new "average" delta for the next iteration
gradients_sq_new = [
rho * g_sq + (1 - rho) * (g**2)
for g_sq, g in zip(gradients_sq, gradients)
]
# calculates the step in direction. The square root is an approximation to getting the RMS for the average value
deltas = [
(T.sqrt(d_sq + eps) / T.sqrt(g_sq + eps)) * grad
for d_sq, g_sq, grad in zip(deltas_sq, gradients_sq_new, gradients)
]
# calculates the new "average" deltas for the next step.
deltas_sq_new = [
rho * d_sq + (1 - rho) * (d**2)
for d_sq, d in zip(deltas_sq, deltas)
]
# Prepare it as a list f
gradient_sq_updates = zip(gradients_sq, gradients_sq_new)
deltas_sq_updates = zip(deltas_sq, deltas_sq_new)
parameters_updates = [(p, p - d) for p, d in zip(parameters, deltas)]
return gradient_sq_updates + deltas_sq_updates + parameters_updates
updates = AdaDelta_updates(params, grads, decay, 1e-6)
train_model = theano.function(
[index], [cost, posi_simi, nega_simi],
updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
# shuffle(train_batch_start)#shuffle training data
corr_train = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
((iter % train_size) * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
cost_average, posi_simi, nega_simi = train_model(batch_start)
if posi_simi > nega_simi:
corr_train += 1
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate:' + str(
corr_train * 100.0 / train_size)
if iter % validation_frequency == 0:
corr_test = 0
for i in test_batch_start:
cost, posi_simi, nega_simi = test_model(i)
if posi_simi > nega_simi:
corr_test += 1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = corr_test * 1.0 / test_size
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.5,
n_epochs=2000,
batch_size=500,
emb_size=300,
hidden_size=300,
L2_weight=0.0001,
para_len_limit=700,
q_len_limit=40):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist = load_train(
para_len_limit, q_len_limit)
train_size = len(train_para_list)
if train_size != len(train_Q_list) or train_size != len(
train_label_list) or train_size != len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist = load_dev_or_test(
word2id, para_len_limit, q_len_limit)
test_size = len(test_para_list)
if test_size != len(test_Q_list) or test_size != len(
test_mask) or test_size != len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
id2word = {y: x for x, y in overall_word2id.iteritems()}
word2vec = load_word2vec()
rand_values = random_value_normal((overall_vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extraF = T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
paragraph_input = embeddings[paragraph.flatten()].reshape(
(paragraph.shape[0], paragraph.shape[1], emb_size)).transpose(
(0, 2, 1)) # (batch_size, emb_size, maxparalen)
#
# # BdGRU(rng, str(0), shape, X, mask, is_train = 1, batch_size = 1, p = 0.5)
#
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b = create_GRU_para(rng, emb_size, hidden_size)
paragraph_para = [U1, W1, b1, U1_b, W1_b, b1_b]
paragraph_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=paragraph_input,
Mask=para_mask,
hidden_dim=hidden_size,
U=U1,
W=W1,
b=b1,
Ub=U1_b,
Wb=W1_b,
bb=b1_b)
para_reps = paragraph_model.output_tensor #(batch, emb, para_len)
Qs_emb = embeddings[questions.flatten()].reshape(
(questions.shape[0], questions.shape[1], emb_size)).transpose(
(0, 2, 1)) #(#questions, emb_size, maxsenlength)
UQ, WQ, bQ = create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b = create_GRU_para(rng, emb_size, hidden_size)
Q_para = [UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
questions_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=Qs_emb,
Mask=q_mask,
hidden_dim=hidden_size,
U=UQ,
W=WQ,
b=bQ,
Ub=UQ_b,
Wb=WQ_b,
bb=bQ_b)
questions_reps = questions_model.output_sent_rep_maxpooling.reshape(
(batch_size, 1, hidden_size)) #(batch, 2*out_size)
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
#attention distributions
W_a1 = create_ensemble_para(
rng, hidden_size,
hidden_size) # init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size + 3) # 3 extra features
norm_W_a1 = normalize_matrix(W_a1)
norm_W_a2 = normalize_matrix(W_a2)
norm_U_a = normalize_matrix(U_a)
LR_b = theano.shared(
value=numpy.zeros((2, ),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b',
borrow=True)
attention_paras = [W_a1, W_a2, U_a, LR_b]
transformed_para_reps = T.tanh(
T.dot(para_reps.transpose((0, 2, 1)), norm_W_a2))
transformed_q_reps = T.tanh(T.dot(questions_reps, norm_W_a1))
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both = 0.5 * (transformed_para_reps + transformed_q_reps)
prior_att = T.concatenate([add_both, normalize_matrix(extraF)], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices = para_mask.flatten().nonzero()[0]
layer3 = LogisticRegression(rng,
input=prior_att.reshape(
(batch_size * prior_att.shape[1],
hidden_size + 3)),
n_in=hidden_size + 3,
n_out=2,
W=norm_U_a,
b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.mean(
T.log(layer3.p_y_given_x)
[valid_indices,
labels.flatten()[valid_indices]]) #[T.arange(y.shape[0]), y])
distributions = layer3.p_y_given_x[:, -1].reshape(
(batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
masked_dis = distributions * para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [embeddings] + paragraph_para + Q_para + attention_paras
L2_reg = L2norm_paraList(
[embeddings, U1, W1, U1_b, W1_b, UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost = error #+L2_weight*L2_reg
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[paragraph, questions, labels, para_mask, q_mask, extraF],
error,
updates=updates,
on_unused_input='ignore')
test_model = theano.function(
[paragraph, questions, para_mask, q_mask, extraF],
masked_dis,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
# remain_test=test_size%batch_size
test_batch_start = list(
numpy.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_exact_acc = 0.0
cost_i = 0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#shuffle(train_batch_start)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i += train_model(
np.asarray(train_para_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_Q_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_label_list[para_id:para_id + batch_size],
dtype='int32'),
np.asarray(train_para_mask[para_id:para_id + batch_size],
dtype=theano.config.floatX),
np.asarray(train_mask[para_id:para_id + batch_size],
dtype=theano.config.floatX),
np.asarray(train_feature_matrixlist[para_id:para_id +
batch_size],
dtype=theano.config.floatX))
#print iter
if iter % 10 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match = 0.0
q_amount = 0
for test_para_id in test_batch_start:
distribution_matrix = test_model(
np.asarray(test_para_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(
test_feature_matrixlist[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list = test_text_list[
test_para_id:test_para_id + batch_size]
para_gold_ansset_list = q_ansSet_list[
test_para_id:test_para_id + batch_size]
paralist_extra_features = test_feature_matrixlist[
test_para_id:test_para_id + batch_size]
sub_para_mask = test_para_mask[test_para_id:test_para_id +
batch_size]
para_len = len(test_para_wordlist_list[0])
if para_len != len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(
distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount += batch_size
# print q_size
# print test_para_word_list
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans = extract_ansList_attentionList(
test_para_wordlist_list[q], distribution_matrix[q],
np.asarray(paralist_extra_features[q],
dtype=theano.config.floatX),
sub_para_mask[q])
q_gold_ans_set = para_gold_ansset_list[q]
F1 = MacroF1(pred_ans, q_gold_ans_set)
exact_match += F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
exact_acc = exact_match / q_amount
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
print 'current average F1:', exact_acc, '\t\tmax F1:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.005, n_epochs=2000, batch_size=300, test_batch_size=400, emb_size=50, hidden_size=300, HL_hidden_size=200,
L2_weight=0.0001, train_size=None, test_size=None, batch_size_pred=400, trichar_len=15,char_emb_size=50,
para_len=101, question_len=20, c_len=1, model_type='train'):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/Users/cisintern/hs/l/workhs/yin/20170320/';
storePath='/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id={}
trichar2id={}
word2id['UNK']=0 # use it to pad
#word2id, trichar2id, questions,questions_mask,paras,paras_mask,labels, isInQ_para, paras_shape, questions_shape, types, types_shape,question_trichar_ids,question_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks
word2id, trichar2id,train_questions,train_questions_mask,train_paras,train_paras_mask,train_labels, train_islabels, train_paras_shape, train_questions_shape, train_types, train_types_shape,train_question_trichar_ids,train_question_trichar_masks,train_para_trichar_ids,train_para_trichar_masks,train_type_trichar_ids,train_type_trichar_masks=load_SQUAD_hinrich_v4(train_size, para_len, question_len, trichar_len, word2id,trichar2id, rootPath+'trn20170320.txt')
word2id, trichar2id,test_questions,test_questions_mask,test_paras,test_paras_mask,test_labels, test_islabels, test_paras_shape, test_questions_shape, test_types, test_types_shape,test_question_trichar_ids,test_question_trichar_masks,test_para_trichar_ids,test_para_trichar_masks,test_type_trichar_ids,test_type_trichar_masks=load_SQUAD_hinrich_v4(test_size, para_len, question_len, trichar_len,word2id, trichar2id, rootPath+'dev.big.20170320.txt')
word2id, trichar2id,test_questions,test_questions_mask,test_paras,test_paras_mask,test_labels, test_islabels, test_paras_shape, test_questions_shape, test_types, test_types_shape,test_question_trichar_ids,test_question_trichar_masks,test_para_trichar_ids,test_para_trichar_masks,test_type_trichar_ids,test_type_trichar_masks=load_SQUAD_hinrich_v4(test_size, para_len, question_len, trichar_len,word2id, trichar2id, rootPath+'dev20170320.txt')
print 'word2id size for bigger dataset:', len(word2id), 'trichar size:', len(trichar2id)
train_size=len(train_questions)
test_size = len(test_questions) #50010#
train_questions = np.asarray(train_questions, dtype='int32')
train_questions_shape = np.asarray(train_questions_shape, dtype='int32')
train_questions_mask = np.asarray(train_questions_mask, dtype=theano.config.floatX)
train_paras = np.asarray(train_paras, dtype='int32')
train_paras_shape = np.asarray(train_paras_shape, dtype='int32')
train_paras_mask = np.asarray(train_paras_mask, dtype=theano.config.floatX)
train_types = np.asarray(train_types, dtype='int32')
train_types_shape = np.asarray(train_types_shape, dtype='int32')
# train_c_ids = np.asarray(train_c_ids, dtype='int32')
# train_c_ids_shape = np.asarray(train_c_ids_shape, dtype='int32')
# train_c_masks = np.asarray(train_c_masks, dtype=theano.config.floatX)
train_islabels = np.asarray(train_islabels, dtype=theano.config.floatX)
# train_c_heads = np.asarray(train_c_heads, dtype='int32')
# train_c_tails = np.asarray(train_c_tails, dtype='int32')
train_labels = np.asarray(train_labels, dtype='int32')
#train_question_trichar_ids,train_question_trichar_masks,train_para_trichar_ids,train_para_trichar_masks,train_type_trichar_ids,train_type_trichar_masks
train_question_trichar_ids = np.asarray(train_question_trichar_ids, dtype='int32')
train_question_trichar_masks = np.asarray(train_question_trichar_masks, dtype=theano.config.floatX)
train_para_trichar_ids = np.asarray(train_para_trichar_ids, dtype='int32')
train_para_trichar_masks = np.asarray(train_para_trichar_masks, dtype=theano.config.floatX)
train_type_trichar_ids = np.asarray(train_type_trichar_ids, dtype='int32')
train_type_trichar_masks = np.asarray(train_type_trichar_masks, dtype=theano.config.floatX)
test_questions = np.asarray(test_questions, dtype='int32')
test_questions_shape = np.asarray(test_questions_shape, dtype='int32')
test_questions_mask = np.asarray(test_questions_mask, dtype=theano.config.floatX)
test_paras = np.asarray(test_paras, dtype='int32')
test_paras_shape = np.asarray(test_paras_shape, dtype='int32')
test_paras_mask = np.asarray(test_paras_mask, dtype=theano.config.floatX)
test_types = np.asarray(test_types, dtype='int32')
test_types_shape = np.asarray(test_types_shape, dtype='int32')
# test_c_ids = np.asarray(test_c_ids, dtype='int32')
# test_c_ids_shape = np.asarray(test_c_ids_shape, dtype='int32')
# test_c_masks = np.asarray(test_c_masks, dtype=theano.config.floatX)
test_islabels = np.asarray(test_islabels, dtype=theano.config.floatX)
# test_c_heads = np.asarray(test_c_heads, dtype='int32')
# test_c_tails = np.asarray(test_c_tails, dtype='int32')
test_labels = np.asarray(test_labels, dtype='int32')
test_question_trichar_ids = np.asarray(test_question_trichar_ids, dtype='int32')
test_question_trichar_masks = np.asarray(test_question_trichar_masks, dtype=theano.config.floatX)
test_para_trichar_ids = np.asarray(test_para_trichar_ids, dtype='int32')
test_para_trichar_masks = np.asarray(test_para_trichar_masks, dtype=theano.config.floatX)
test_type_trichar_ids = np.asarray(test_type_trichar_ids, dtype='int32')
test_type_trichar_masks = np.asarray(test_type_trichar_masks, dtype=theano.config.floatX)
overall_vocab_size=len(word2id)
print 'train size:', train_size, 'test size:', test_size, 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size, emb_size), theano.config.floatX, rng)
rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
overall_trichar_size = len(trichar2id)
char_rand_values=random_value_normal((overall_trichar_size, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
para=T.imatrix() #(2*batch, len)
para_shape = T.imatrix()
para_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_shape = T.imatrix()
q_mask=T.fmatrix() #(2*batch, len_q)
islabels = T.fmatrix()
labels=T.ivector() #batch
types=T.imatrix()
types_shape=T.imatrix()
q_trichar_ids = T.imatrix()
q_trichar_masks =T.fmatrix()
para_trichar_ids = T.imatrix()
para_trichar_masks =T.fmatrix()
type_trichar_ids = T.imatrix()
type_trichar_masks =T.fmatrix()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = para.shape[0]
paragraph_input = embeddings[para.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input = embeddings[q.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
q_types = embeddings[types.flatten()].reshape((true_batch_size, 2, emb_size)).transpose((0, 2,1))
paragraph_input_shape = embeddings[para_shape.flatten()].reshape((true_batch_size, para_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, para_len)
q_input_shape = embeddings[q_shape.flatten()].reshape((true_batch_size, question_len, emb_size)).transpose((0, 2,1)) # (batch, emb_size, question_len)
q_types_shape = embeddings[types_shape.flatten()].reshape((true_batch_size, 2, emb_size)).transpose((0, 2,1))
paragraph_input_trichar = char_embeddings[para_trichar_ids.flatten()].reshape((true_batch_size, para_len*trichar_len, char_emb_size)) #(batch, char_emb_size, para_len*trichar_len)
q_input_trichar = char_embeddings[q_trichar_ids.flatten()].reshape((true_batch_size, question_len*trichar_len, char_emb_size)) # (batch, emb_size, question_len)
q_types_trichar = char_embeddings[type_trichar_ids.flatten()].reshape((true_batch_size, 2*trichar_len, char_emb_size))
#sum up trichar emb as word level embs
paragraph_input_trichar=T.sum((paragraph_input_trichar*para_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, para_len, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,para_len)
q_input_trichar=T.sum((q_input_trichar*q_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, question_len, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,q_len)
q_types_trichar=T.sum((q_types_trichar*type_trichar_masks.dimshuffle(0,1,'x')).reshape((true_batch_size, 2, trichar_len,char_emb_size)),axis=2).dimshuffle(0,2,1) #(true_batch_size, char_emb_size,2)
#concatenate word emb with shape emb
q_input = T.concatenate([q_input,q_input_shape, q_input_trichar],axis=1) #(batch, 2*emb_size+char_emb_size, q_len)
paragraph_input = T.concatenate([paragraph_input,paragraph_input_shape, paragraph_input_trichar,islabels.dimshuffle(0,'x',1)],axis=1)#(batch, 2*emb_size+char_emb_size+1, para_len)
q_types_input = T.sum(T.concatenate([q_types,q_types_shape,q_types_trichar],axis=1), axis=2) #(batch, 2*emb+char_emb_size)
fwd_LSTM_para_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size+1, hidden_size)
bwd_LSTM_para_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size+1, hidden_size)
paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
paragraph_reps_tensor3=paragraph_model.output_tensor #(batch, 2*hidden, paralen)
fwd_LSTM_q_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size, hidden_size)
bwd_LSTM_q_dict=create_LSTM_para(rng, 2*emb_size+char_emb_size, hidden_size)
question_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(q_input, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
q_reps=questions_model.output_sent_rep_maxpooling #(batch, 2*hidden)
#interaction
batch_ids=T.arange(true_batch_size)
# c_heads=theano.shared(value=np.asarray([(para_len-1)/2]*batch_size, dtype='int32'), borrow=True)
c_heads = T.repeat(theano.shared(value=np.asarray([(para_len-1)/2], dtype='int32'), borrow=True), true_batch_size)
c_tails=c_heads+1
c_heads_reps=paragraph_reps_tensor3[batch_ids,:,c_heads] #(batch, 2*hidden)
c_tails_reps=paragraph_reps_tensor3[batch_ids,:,c_tails] #(batch, 2*hidden)
candididates_reps=T.concatenate([c_heads_reps, c_tails_reps], axis=1) #(batch, 4*hidden)
context_l=paragraph_model.forward_output[batch_ids,:,c_heads-1] #(batch, hidden)
context_r=paragraph_model.backward_output[batch_ids,:,c_tails+1]#(batch, hidden)
#glove level average
# c_input = embeddings[c_ids.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
# c_input_shape = embeddings[c_ids_shape.flatten()].reshape((true_batch_size, c_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
# c_input = T.concatenate([c_input,c_input_shape],axis=1)
c_sum = paragraph_input[:,:-1,(para_len-1)/2]#(batch, 2*emb_size+char_emb)
c_sum_with_isInQLabel = paragraph_input[:,:,(para_len-1)/2]
# e_input = embeddings[e_ids.flatten()].reshape((true_batch_size, e_len, emb_size)).transpose((0, 2,1)) #(batch, emb_size, c_len)
q_sum = T.sum(q_input*q_mask.dimshuffle(0,'x',1), axis=2) #(batch, 2*emb_size+char_emb_size)
# average_Q_batch = q_sum/T.sqrt(T.sum(q_sum**2, axis=1)+1e-20).dimshuffle(0,'x')
HL_layer_1_input_size=2*hidden_size+4*hidden_size+(2*emb_size+char_emb_size+1)+(2*emb_size+char_emb_size)+1+hidden_size+hidden_size+(2*emb_size+char_emb_size)+1
cosine_Qtype_cand = cosine_row_wise_twoMatrix(q_types_input, c_sum).dimshuffle(0,'x') #(batch, 1)
#, average_E_batch, average_C_batch, average_Q_batch
HL_layer_1_input = T.concatenate([q_reps, candididates_reps, c_sum_with_isInQLabel, q_sum, islabels[:,(para_len-1)/2:(para_len-1)/2+1], context_l, context_r,
q_types_input,
cosine_Qtype_cand], axis=1)
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=HL_hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=HL_hidden_size, n_out=HL_hidden_size, activation=T.tanh)
LR_input= T.concatenate([HL_layer_1.output, HL_layer_2.output, islabels[:,(para_len-1)/2:(para_len-1)/2+1], cosine_Qtype_cand], axis=1) #(batch, char_HL_hidden_size+HL_hidden_size)
LR_input_size= HL_hidden_size+HL_hidden_size+1+1#HL_layer_1_input_size+2*HL_hidden_size
U_a = create_ensemble_para(rng, 2, LR_input_size) # the weight matrix hidden_size*2
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=np.zeros((2,),dtype=theano.config.floatX),name='char_LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=2, W=norm_U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = LR_para+[embeddings,char_embeddings]+paragraph_para+question_para+HL_layer_1.params+HL_layer_2.params
# load_model_from_file(storePath+'Best_Paras_HS_20170316_0.760357142857', params)
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
# L2_reg = L2norm_paraList(params)
cost=loss#+1e-6*L2_reg
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([para, para_shape, para_mask,q,q_shape, q_mask,islabels, labels, types, types_shape, q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks], cost, updates=updates,on_unused_input='ignore')
# train_model_pred = theano.function([para, para_mask, c_ids,c_mask,e_ids,e_mask, c_heads, c_tails, l_heads, l_tails, e_heads, e_tails, q, q_mask,labels], layer_LR.y_pred, on_unused_input='ignore')
test_model = theano.function([para, para_shape, para_mask, q,q_shape, q_mask,islabels, labels, types, types_shape,q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks], [layer_LR.errors(labels),layer_LR.prop_for_posi], on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
# n_train_batches_pred=train_size/batch_size_pred #batch_size means how many pairs
# train_batch_start_pred=list(np.arange(n_train_batches_pred)*batch_size_pred)+[train_size-batch_size_pred]
n_test_batches=test_size/test_batch_size #batch_size means how many pairs
n_test_remain=test_size%test_batch_size #batch_size means how many pairs
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_acc=0.0
cost_i=0.0
train_ids = range(train_size)
# train_ids_pred = range(train_size)
best_test_statistic=defaultdict(int)
# best_train_statistic=defaultdict(int)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
# print train_ids[:100]
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list = train_ids[para_id:para_id+batch_size]
# print 'train_labels[train_id_list]:', train_labels[train_id_list]
if model_type=='train':
#para, para_shape, para_mask,q,q_shape, q_mask,islabels, labels, types, types_shape, q_trichar_ids,q_trichar_masks,para_trichar_ids,para_trichar_masks,type_trichar_ids,type_trichar_masks
cost_i+= train_model(
train_paras[train_id_list],
train_paras_shape[train_id_list],
train_paras_mask[train_id_list],
train_questions[train_id_list],
train_questions_shape[train_id_list],
train_questions_mask[train_id_list],
train_islabels[train_id_list],
train_labels[train_id_list],
train_types[train_id_list],
train_types_shape[train_id_list],
train_question_trichar_ids[train_id_list],
train_question_trichar_masks[train_id_list],
train_para_trichar_ids[train_id_list],
train_para_trichar_masks[train_id_list],
train_type_trichar_ids[train_id_list],
train_type_trichar_masks[train_id_list])
#print iter
if iter%10 ==0:
print 'Epoch ', epoch, 'iter '+str(iter)+'/'+str(len(train_batch_start))+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
print 'Testing...'
error=0
test_statistic=defaultdict(int)
if model_type=='test':
writefile=open(storePath+'predictions_20170317.txt', 'w')
for id, test_para_id in enumerate(test_batch_start):
test_id_list = range(test_para_id, test_para_id+test_batch_size)
# print 'test_id_list:',test_id_list
# print 'test_c_heads[test_id_list]', test_c_heads[test_id_list]
# gold_labels_list = test_labels_3c[test_para_id:test_para_id+test_batch_size]
error_i, preds_i= test_model(
test_paras[test_id_list],
test_paras_shape[test_id_list],
test_paras_mask[test_id_list],
test_questions[test_id_list],
test_questions_shape[test_id_list],
test_questions_mask[test_id_list],
test_islabels[test_id_list],
test_labels[test_id_list],
test_types[test_id_list],
test_types_shape[test_id_list],
test_question_trichar_ids[test_id_list],
test_question_trichar_masks[test_id_list],
test_para_trichar_ids[test_id_list],
test_para_trichar_masks[test_id_list],
test_type_trichar_ids[test_id_list],
test_type_trichar_masks[test_id_list])
if model_type=='test':
if id < len(test_batch_start)-1:
writefile.write('\n'.join(map(str,list(preds_i)))+'\n')
else:
writefile.write('\n'.join(map(str,list(preds_i)[-n_test_remain:]))+'\n')
error+=error_i
# for ind, gold_label in enumerate(gold_labels_list):
# test_statistic[(gold_label, preds_i[ind])]+=1
if model_type=='test':
writefile.close()
acc=1.0-error*1.0/len(test_batch_start)
# acc= (test_statistic.get((1,1),0)+test_statistic.get((0,0),0))*1.0/(test_statistic.get((1,1),0)+test_statistic.get((0,0),0)+test_statistic.get((1,0),0)+test_statistic.get((0,1),0))
if acc> max_acc:
max_acc=acc
# best_test_statistic=test_statistic
if model_type=='train':
store_model_to_file(storePath+'Best_Paras_HS_20170324_'+str(max_acc), params)
print 'Finished storing best params at:', max_acc
print 'current average acc:', acc, '\t\tmax acc:', max_acc#, '\ttest_statistic:', test_statistic
# print '\t\t\t\tbest statistic:', best_test_statistic
if model_type=='test':
exit(0)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=10, hidden_size=10,
L2_weight=0.0001, para_len_limit=400, q_len_limit=40, max_EM=0.217545454546):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
norm_extraF=normalize_matrix(extraF)
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size+3) # 3 extra features
LR_b = theano.shared(value=numpy.zeros((2,),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b', borrow=True)
attention_paras=[W_a1, W_a2, U_a, LR_b]
params = [embeddings]+paragraph_para+Q_para+attention_paras
load_model_from_file(rootPath+'Best_Paras_conv_0.217545454545', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((paragraph.shape[0], paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input=T.concatenate([paragraph_input, norm_extraF.dimshuffle((0,2,1))], axis=1)
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape((questions.shape[0], questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
questions_reps_tensor=questions_model.output_tensor
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#use CNN for question modeling
# Qs_emb_tensor4=Qs_emb.dimshuffle((0,'x', 1,2)) #(batch_size, 1, emb+3, maxparalen)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, 5))
# Q_conv_para=[conv_W, conv_b]
# conv_model = Conv_with_input_para(rng, input=Qs_emb_tensor4,
# image_shape=(batch_size, 1, emb_size, q_len_limit),
# filter_shape=(hidden_size, 1, emb_size, 5), W=conv_W, b=conv_b)
# conv_output=conv_model.narrow_conv_out.reshape((batch_size, hidden_size, q_len_limit-5+1)) #(batch, 1, hidden_size, maxparalen-1)
# gru_mask=(q_mask[:,:-4]*q_mask[:,1:-3]*q_mask[:,2:-2]*q_mask[:,3:-1]*q_mask[:,4:]).reshape((batch_size, 1, q_len_limit-5+1))
# masked_conv_output=conv_output*gru_mask
# questions_conv_reps=T.max(masked_conv_output, axis=2).reshape((batch_size, 1, hidden_size))
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix=para_matrix.T
interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T) #(len, para_len)
batch_q_reps, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
#attention distributions
norm_W_a1=normalize_matrix(W_a1)
norm_W_a2=normalize_matrix(W_a2)
norm_U_a=normalize_matrix(U_a)
transformed_para_reps=T.maximum(T.dot(para_reps.transpose((0, 2,1)), norm_W_a2),0.0) #relu
transformed_q_reps=T.maximum(T.dot(batch_q_reps.transpose((0, 2,1)), norm_W_a1),0.0)
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both=transformed_para_reps+transformed_q_reps
# U_c, W_c, b_c=create_GRU_para(rng, hidden_size, hidden_size)
# U_c_b, W_c_b, b_c_b=create_GRU_para(rng, hidden_size, hidden_size)
# accumu_para=[U_c, W_c, b_c, U_c_b, W_c_b, b_c_b]
# accumu_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_both.transpose((0,2,1)), Mask=para_mask, hidden_dim=hidden_size,U=U_c,W=W_c,b=b_c,Ub=U_c_b,Wb=W_c_b,bb=b_c_b)
# accu_both=accumu_model.output_tensor.transpose((0,2,1))
prior_att=T.concatenate([add_both, norm_extraF], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices=para_mask.flatten().nonzero()[0]
layer3=LogisticRegression(rng, input=prior_att.reshape((batch_size*prior_att.shape[1], hidden_size+3)), n_in=hidden_size+3, n_out=2, W=norm_U_a, b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.sum(T.log(layer3.p_y_given_x)[valid_indices, labels.flatten()[valid_indices]])#[T.arange(y.shape[0]), y])
distributions=layer3.p_y_given_x[:,-1].reshape((batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
# masked_dis=(distributions+ConvGRU_1_dis_into_unigram)*para_mask
masked_dis=distributions*para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=error#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([paragraph, questions,labels, para_mask, q_mask, extraF], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], masked_dis, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
np.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_label_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
np.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
np.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
np.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
distribution_matrix=test_model(
np.asarray(test_para_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_feature_matrixlist[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+batch_size]
para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+batch_size]
paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
para_len=len(test_para_wordlist_list[0])
if para_len!=len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount+=batch_size
# print q_size
# print test_para_word_list
Q_list_inword=test_Q_list_word[test_para_id:test_para_id+batch_size]
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], np.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
q_gold_ans_set=para_gold_ansset_list[q]
# print test_para_wordlist_list[q]
# print Q_list_inword[q]
# print pred_ans.encode('utf8'), q_gold_ans_set
if pred_ans in q_gold_ans_set:
exact_match+=1
F1=MacroF1(pred_ans, q_gold_ans_set)
F1_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/q_amount
exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_conv_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, word_nkerns=300, batch_size=1, window_width=[3,3],
emb_size=300,
margin=0.5, L2_weight=0.0003, Div_reg=0.03, update_freq=1, norm_threshold=5.0, max_truncate=40,
max_relation_len=6, max_Q_len=30,
neg_all=100, train_size=69967, test_size=19953, mark='_RC_newdata'): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/home/wyin/Datasets/SimpleQuestions_v2/relation_classification/'
triple_files=['train.replace_ne.withpoolwenpengFormat.txt', 'test.replace_ne.withpoolwenpengFormat.txt']
rng = numpy.random.RandomState(23455)
datasets, datasets_test, length_per_example_train, length_per_example_test, vocab_size=load_train(triple_files[0], triple_files[1], max_relation_len, max_Q_len, train_size, test_size, mark)#max_char_len, max_des_len, max_relation_len, max_Q_len
print 'vocab_size:', vocab_size
train_data=datasets
# valid_data=datasets[1]
test_data=datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
train_relations=train_data[0]
train_relation_lengths=train_data[1]
train_remainQ_word_ids=train_data[2]
train_remainQ_word_len=train_data[3]
test_relations=test_data[0]
test_relation_lengths=test_data[1]
test_remainQ_word_ids=test_data[2]
test_remainQ_word_len=test_data[3]
train_sizes=[len(train_relations),len(train_relation_lengths),len(train_remainQ_word_ids), len(train_remainQ_word_len)]
if sum(train_sizes)/len(train_sizes)!=train_size:
print 'weird size:', train_sizes
exit(0)
test_sizes=[len(test_relations),len(test_relation_lengths), len(test_remainQ_word_ids),len(test_remainQ_word_len)]
if sum(test_sizes)/len(test_sizes)!=test_size:
print 'weird size:', test_sizes
exit(0)
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
# indices_train_pos_entity_char=theano.shared(numpy.asarray(train_pos_entity_char, dtype='int32'), borrow=True)
# indices_train_pos_entity_des=theano.shared(numpy.asarray(train_pos_entity_des, dtype='int32'), borrow=True)
# indices_train_relations=theano.shared(numpy.asarray(train_relations, dtype='int32'), borrow=True)
# indices_train_entity_char_lengths=theano.shared(numpy.asarray(train_entity_char_lengths, dtype='int32'), borrow=True)
# indices_train_entity_des_lengths=theano.shared(numpy.asarray(train_entity_des_lengths, dtype='int32'), borrow=True)
# indices_train_relation_lengths=theano.shared(numpy.asarray(train_relation_lengths, dtype='int32'), borrow=True)
# indices_train_mention_char_ids=theano.shared(numpy.asarray(train_mention_char_ids, dtype='int32'), borrow=True)
# indices_train_remainQ_word_ids=theano.shared(numpy.asarray(train_remainQ_word_ids, dtype='int32'), borrow=True)
# indices_train_mention_char_lens=theano.shared(numpy.asarray(train_mention_char_lens, dtype='int32'), borrow=True)
# indices_train_remainQ_word_len=theano.shared(numpy.asarray(train_remainQ_word_len, dtype='int32'), borrow=True)
# indices_train_entity_scores=theano.shared(numpy.asarray(train_entity_scores, dtype=theano.config.floatX), borrow=True)
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
# rand_values=load_word2vec_to_init(rand_values, rootPath+'word_emb'+mark+'.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.iscalar()
rel_word_ids_M=T.imatrix()
rel_word_lens_M=T.imatrix()
q_word_ids_f=T.ivector()
q_word_lens_f=T.ivector()
filter_size=(emb_size,window_width[0])
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
word_filter_shape=(word_nkerns, 1, filter_size[0], filter_size[1])
q_rel_conv_W, q_rel_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
params = [embeddings,q_rel_conv_W, q_rel_conv_b]
q_rel_conv_W_into_matrix=q_rel_conv_W.reshape((q_rel_conv_W.shape[0], q_rel_conv_W.shape[2]*q_rel_conv_W.shape[3]))
# load_model_from_file(rootPath, params, '')
def SimpleQ_matches_Triple(rel_word_ids_f,rel_word_lens_f):
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
overall_simi=cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(
SimpleQ_matches_Triple,
sequences=[rel_word_ids_M,rel_word_lens_M])
posi_simi=simi_list[0]
nega_simies=simi_list[1:]
loss_simi_list=T.maximum(0.0, margin-posi_simi.reshape((1,1))+nega_simies)
loss_simi=T.sum(loss_simi_list)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((embeddings** 2).sum()+(q_rel_conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg= Diversify_Reg(q_rel_conv_W_into_matrix)
cost=loss_simi+L2_weight*L2_reg+Div_reg*diversify_reg
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([rel_word_ids_M, rel_word_lens_M, q_word_ids_f, q_word_lens_f], [loss_simi, simi_list],on_unused_input='ignore')
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
# updates.append((acc_i, acc))
if param_i == embeddings:
updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))[0], theano.shared(numpy.zeros(emb_size))))) #Ada
else:
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([rel_word_ids_M, rel_word_lens_M, q_word_ids_f, q_word_lens_f], [loss_simi, cost],updates=updates, on_unused_input='ignore')
# train_model = theano.function([index, chosed_indices], [loss_simi, cost], updates=updates,
# givens={
# rel_word_ids_M : indices_train_relations[index].reshape((neg_all, max_relation_len))[chosed_indices].reshape((train_neg_size, max_relation_len)),
# rel_word_lens_M : indices_train_relation_lengths[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
# q_word_ids_M : indices_train_remainQ_word_ids[index].reshape((neg_all, max_Q_len))[chosed_indices].reshape((train_neg_size, max_Q_len)),
# q_word_lens_M : indices_train_remainQ_word_len[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3))
#
# }, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
best_test_accu=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
for jj in range(train_size):
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#print batch_start
train_rel_word_ids_M = numpy.asarray(train_relations[jj], dtype='int32').reshape((length_per_example_train[jj], max_relation_len))
train_rel_word_lens_M = numpy.asarray(train_relation_lengths[jj], dtype='int32').reshape((length_per_example_train[jj], 3))
train_q_word_ids_M = numpy.asarray(train_remainQ_word_ids[jj], dtype='int32')#.reshape((length_per_example_train[jj], max_Q_len))
train_q_word_lens_M = numpy.asarray(train_remainQ_word_len[jj], dtype='int32')#.reshape((length_per_example_train[jj], 3))
loss_simi_i, cost_i=train_model(train_rel_word_ids_M, train_rel_word_lens_M,train_q_word_ids_M, train_q_word_lens_M)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+'\tloss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
#if iter ==1:
# exit(0)
#
if iter > 59999 and iter % 10000 == 0:
test_loss=[]
succ=0
for i in range(test_size):
# print 'testing', i, '...'
#prepare data
test_rel_word_ids_M = numpy.asarray(test_relations[i], dtype='int32').reshape((length_per_example_test[i], max_relation_len))
test_rel_word_lens_M = numpy.asarray(test_relation_lengths[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(test_remainQ_word_ids[i], dtype='int32')#.reshape((length_per_example_test[i], max_Q_len))
test_q_word_lens_M = numpy.asarray(test_remainQ_word_len[i], dtype='int32')#.reshape((length_per_example_test[i], 3))
loss_simi_i,simi_list_i=test_model(test_rel_word_ids_M, test_rel_word_lens_M,test_q_word_ids_M, test_q_word_lens_M)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if simi_list_i[0]>=max(simi_list_i[1:]):
succ+=1
# print 'testing', i, '...acc:', succ*1.0/(i+1)
succ=(succ+20610-test_size)*1.0/20610
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test accu of best '
'model %f') %
(epoch, minibatch_index, n_train_batches,succ))
if best_test_accu< succ:
best_test_accu=succ
store_model_to_file(rootPath, params, mark)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, batch_size=500, test_batch_size=500, emb_size=300, hidden_size=300,
L2_weight=0.0001, margin=0.5,
train_size=4000000, test_size=1000,
max_context_len=25, max_span_len=7, max_q_len=40, max_EM=0.052):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = np.random.RandomState(23455)
word2id,train_questions,train_questions_mask,train_lefts,train_lefts_mask,train_spans,train_spans_mask,train_rights,train_rights_mask=load_SQUAD_hinrich(train_size, max_context_len, max_span_len, max_q_len)
test_ground_truth,test_candidates,test_questions,test_questions_mask,test_lefts,test_lefts_mask,test_spans,test_spans_mask,test_rights,test_rights_mask=load_dev_hinrich(word2id, test_size, max_context_len, max_span_len, max_q_len)
overall_vocab_size=len(word2id)
print 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, np.random.RandomState(1234))
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
left=T.imatrix() #(2*batch, len)
left_mask=T.fmatrix() #(2*batch, len)
span=T.imatrix() #(2*batch, span_len)
span_mask=T.fmatrix() #(2*batch, span_len)
right=T.imatrix() #(2*batch, len)
right_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
GRU1_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U2, W2, b2=create_GRU_para(rng, hidden_size, hidden_size)
U2_b, W2_b, b2_b=create_GRU_para(rng, hidden_size, hidden_size)
GRU2_para=[U2, W2, b2, U2_b, W2_b, b2_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
attend_para=[W_a1, W_a2]
params = [embeddings]+GRU1_para+attend_para+GRU2_para
# load_model_from_file(rootPath+'Best_Para_dim'+str(emb_size), params)
left_input = embeddings[left.flatten()].reshape((left.shape[0], left.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
span_input = embeddings[span.flatten()].reshape((span.shape[0], span.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_span)
right_input = embeddings[right.flatten()].reshape((right.shape[0], right.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
q_input = embeddings[q.flatten()].reshape((q.shape[0], q.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_q)
left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=left_input, Mask=left_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
left_reps=left_model.output_tensor #(batch, emb, para_len)
span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=span_input, Mask=span_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
span_reps=span_model.output_tensor #(batch, emb, para_len)
right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=right_input, Mask=right_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
right_reps=right_model.output_tensor #(batch, emb, para_len)
q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
q_reps=q_model.output_tensor #(batch, emb, para_len)
#interaction
left_reps_via_q_reps, q_reps_via_left_reps=attention_dot_prod_between_2tensors(left_reps, q_reps)
span_reps_via_q_reps, q_reps_via_span_reps=attention_dot_prod_between_2tensors(span_reps, q_reps)
right_reps_via_q_reps, q_reps_via_right_reps=attention_dot_prod_between_2tensors(right_reps, q_reps)
# q_reps_via_left_reps=attention_dot_prod_between_2tensors(q_reps, left_reps)
# q_reps_via_span_reps=attention_dot_prod_between_2tensors(q_reps, span_reps)
# q_reps_via_right_reps=attention_dot_prod_between_2tensors(q_reps, right_reps)
#combine
origin_W=normalize_matrix(W_a1)
attend_W=normalize_matrix(W_a2)
left_origin_reps=T.dot(left_reps.dimshuffle(0, 2,1), origin_W)
span_origin_reps=T.dot(span_reps.dimshuffle(0, 2,1), origin_W)
right_origin_reps=T.dot(right_reps.dimshuffle(0, 2,1), origin_W)
q_origin_reps=T.dot(q_reps.dimshuffle(0, 2,1), origin_W)
left_attend_q_reps=T.dot(q_reps_via_left_reps.dimshuffle(0, 2,1), attend_W)
span_attend_q_reps=T.dot(q_reps_via_span_reps.dimshuffle(0, 2,1), attend_W)
right_attend_q_reps=T.dot(q_reps_via_right_reps.dimshuffle(0, 2,1), attend_W)
q_attend_left_reps=T.dot(left_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_span_reps=T.dot(span_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_right_reps=T.dot(right_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
add_left=left_origin_reps+q_attend_left_reps #(2*batch, len ,hidden)
add_span=span_origin_reps+q_attend_span_reps
add_right=right_origin_reps+q_attend_right_reps
add_q_by_left=q_origin_reps+left_attend_q_reps
add_q_by_span=q_origin_reps+span_attend_q_reps
add_q_by_right=q_origin_reps+right_attend_q_reps
#second GRU
add_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_left.dimshuffle(0,2,1), Mask=left_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_left_reps=add_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_span.dimshuffle(0,2,1), Mask=span_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_span_reps=add_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_right.dimshuffle(0,2,1), Mask=right_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_right_reps=add_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_left.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_left_reps=add_q_by_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_span.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_span_reps=add_q_by_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_right.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_right_reps=add_q_by_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
paragraph_concat=T.concatenate([add_left_reps, add_span_reps, add_right_reps], axis=1) #(batch, 3*hidden)
question_concat=T.concatenate([add_q_by_left_reps, add_q_by_span_reps, add_q_by_right_reps], axis=1) #(batch, 3*hidden)
simi_list=cosine_row_wise_twoMatrix(paragraph_concat, question_concat) #(2*batch)
pos_simi_vec=simi_list[::2]
neg_simi_vec=simi_list[1::2]
raw_loss=T.maximum(0.0, margin+neg_simi_vec-pos_simi_vec)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=T.sum(raw_loss)#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], simi_list, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
remain_train=train_size%batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size*2)+[train_size*2-batch_size*2] # always ou shu
if remain_train>0:
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
else:
train_batch_start=list(np.arange(n_train_batches)*batch_size)
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_odd_ids = list(np.arange(train_size)*2)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_odd_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_list=[[train_odd_id, train_odd_id+1] for train_odd_id in train_odd_ids[para_id:para_id+batch_size]]
train_id_list=sum(train_id_list,[])
# print train_id_list
cost_i+= train_model(
np.asarray([train_lefts[id] for id in train_id_list], dtype='int32'),
np.asarray([train_lefts_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_spans[id] for id in train_id_list], dtype='int32'),
np.asarray([train_spans_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_rights[id] for id in train_id_list], dtype='int32'),
np.asarray([train_rights_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_questions[id] for id in train_id_list], dtype='int32'),
np.asarray([train_questions_mask[id] for id in train_id_list], dtype=theano.config.floatX))
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
for test_pair_id in range(test_size):
test_example_lefts=test_lefts[test_pair_id]
test_example_lefts_mask=test_lefts_mask[test_pair_id]
test_example_spans=test_spans[test_pair_id]
test_example_spans_mask=test_spans_mask[test_pair_id]
test_example_rights=test_rights[test_pair_id]
test_example_rights_mask=test_rights_mask[test_pair_id]
test_example_questions=test_questions[test_pair_id]
test_example_questions_mask=test_questions_mask[test_pair_id]
test_example_candidates=test_candidates[test_pair_id]
test_example_size=len(test_example_lefts)
# print 'test_pair_id, test_example_size:', test_pair_id, test_example_size
if test_example_size < test_batch_size:
#pad
pad_size=test_batch_size-test_example_size
test_example_lefts+=test_example_lefts[-1:]*pad_size
test_example_lefts_mask+=test_example_lefts_mask[-1:]*pad_size
test_example_spans+=test_example_spans[-1:]*pad_size
test_example_spans_mask+=test_example_spans_mask[-1:]*pad_size
test_example_rights+=test_example_rights[-1:]*pad_size
test_example_rights_mask+=test_example_rights_mask[-1:]*pad_size
test_example_questions+=test_example_questions[-1:]*pad_size
test_example_questions_mask+=test_example_questions_mask[-1:]*pad_size
test_example_candidates+=test_example_candidates[-1:]*pad_size
test_example_size=test_batch_size
n_test_batches=test_example_size/test_batch_size
n_test_remain=test_example_size%test_batch_size
if n_test_remain > 0:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_example_size-test_batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)
all_simi_list=[]
all_cand_list=[]
for test_para_id in test_batch_start:
simi_return_vector=test_model(
np.asarray(test_example_lefts[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_lefts_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_spans[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_spans_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_rights[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_rights_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_questions[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_questions_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
candidate_list=test_example_candidates[test_para_id:test_para_id+test_batch_size]
all_simi_list+=list(simi_return_vector)
all_cand_list+=candidate_list
top1_cand=all_cand_list[np.argsort(all_simi_list)[-1]]
# print top1_cand, test_ground_truth[test_pair_id]
if top1_cand == test_ground_truth[test_pair_id]:
exact_match+=1
F1=macrof1(top1_cand, test_ground_truth[test_pair_id])
# print '\t\t\t', F1
F1_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/test_size
exact_acc=exact_match/test_size
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
# store_model_to_file(params, emb_size)
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Para_'+str(max_EM), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, nkerns=[50,50], batch_size=1, window_width=3,
maxSentLength=64, maxDocLength=60, emb_size=50, hidden_size=200,
L2_weight=0.0065, update_freq=1, norm_threshold=5.0, max_s_length=57, max_d_length=59, margin=1.0, decay=0.95):
maxSentLength=max_s_length+2*(window_width-1)
maxDocLength=max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data,train_size, test_data, test_size, vocab_size=load_MCTest_corpus_DQAAAA(rootPath+'vocab_DQAAAA.txt', rootPath+'mc500.train.tsv_standardlized.txt_DQAAAA.txt', rootPath+'mc500.test.tsv_standardlized.txt_DQAAAA.txt', max_s_length,maxSentLength, maxDocLength)#vocab_size contain train, dev and test
[train_data_D, train_data_Q, train_data_A1, train_data_A2, train_data_A3, train_data_A4, train_Label,
train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A1, train_Length_A2, train_Length_A3, train_Length_A4,
train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3, train_leftPad_A4,
train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A1, train_rightPad_A2, train_rightPad_A3, train_rightPad_A4]=train_data
[test_data_D, test_data_Q, test_data_A1, test_data_A2, test_data_A3, test_data_A4, test_Label,
test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A1, test_Length_A2, test_Length_A3, test_Length_A4,
test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3, test_leftPad_A4,
test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A1, test_rightPad_A2, test_rightPad_A3, test_rightPad_A4]=test_data
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_DQAAAA_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A1= T.lvector()
index_A2= T.lvector()
index_A3= T.lvector()
index_A4= T.lvector()
# y = T.lvector()
len_D=T.lscalar()
len_D_s=T.lvector()
len_Q=T.lscalar()
len_A1=T.lscalar()
len_A2=T.lscalar()
len_A3=T.lscalar()
len_A4=T.lscalar()
left_D=T.lscalar()
left_D_s=T.lvector()
left_Q=T.lscalar()
left_A1=T.lscalar()
left_A2=T.lscalar()
left_A3=T.lscalar()
left_A4=T.lscalar()
right_D=T.lscalar()
right_D_s=T.lvector()
right_Q=T.lscalar()
right_A1=T.lscalar()
right_A2=T.lscalar()
right_A3=T.lscalar()
right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words=(emb_size,window_width)
filter_sents=(nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = debug_print(embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1), 'layer0_D_input')#.dimshuffle(0, 'x', 1, 2)
layer0_Q_input = debug_print(embeddings[index_Q.flatten()].reshape((maxSentLength, emb_size)).transpose(), 'layer0_Q_input')#.dimshuffle(0, 'x', 1, 2)
layer0_A1_input = debug_print(embeddings[index_A1.flatten()].reshape((maxSentLength, emb_size)).transpose(), 'layer0_A1_input')#.dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape((maxSentLength, emb_size)).transpose()#.dimshuffle(0, 'x', 1, 2)
U, W, b=create_GRU_para(rng, emb_size, nkerns[0])
layer0_para=[U, W, b]
# conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
# layer2_para=[conv2_W, conv2_b]
# high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1])
# highW_para=[high_W, high_b]
#load_model(params)
layer0_D = GRU_Tensor3_Input(T=layer0_D_input[left_D:-right_D,:,:],
lefts=left_D_s[left_D:-right_D],
rights=right_D_s[left_D:-right_D],
hidden_dim=nkerns[0],
U=U,W=W,b=b)
layer0_Q = GRU_Matrix_Input(X=layer0_Q_input[:,left_Q:-right_Q], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A1 = GRU_Matrix_Input(X=layer0_A1_input[:,left_A1:-right_A1], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A2 = GRU_Matrix_Input(X=layer0_A2_input[:,left_A2:-right_A2], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A3 = GRU_Matrix_Input(X=layer0_A3_input[:,left_A3:-right_A3], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A4 = GRU_Matrix_Input(X=layer0_A4_input[:,left_A4:-right_A4], word_dim=emb_size, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_D_output=debug_print(layer0_D.output, 'layer0_D.output')
layer0_Q_output=debug_print(layer0_Q.output_vector_mean, 'layer0_Q.output')
layer0_A1_output=debug_print(layer0_A1.output_vector_mean, 'layer0_A1.output')
layer0_A2_output=debug_print(layer0_A2.output_vector_mean, 'layer0_A2.output')
layer0_A3_output=debug_print(layer0_A3.output_vector_mean, 'layer0_A3.output')
layer0_A4_output=debug_print(layer0_A4.output_vector_mean, 'layer0_A4.output')
#before reasoning, do a GRU for doc: d
U_d, W_d, b_d=create_GRU_para(rng, nkerns[0], nkerns[0])
layer_d_para=[U_d, W_d, b_d]
layer_D_GRU = GRU_Matrix_Input(X=layer0_D_output, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_d,W=W_d,b=b_d,bptt_truncate=-1)
#Reasoning Layer 1
repeat_Q=debug_print(T.repeat(layer0_Q_output.reshape((layer0_Q_output.shape[0],1)), maxDocLength, axis=1)[:,:layer_D_GRU.output_matrix.shape[1]], 'repeat_Q')
input_DNN=debug_print(T.concatenate([layer_D_GRU.output_matrix,repeat_Q], axis=0).transpose(), 'input_DNN')#each row is an example
output_DNN1=HiddenLayer(rng, input=input_DNN, n_in=nkerns[0]*2, n_out=nkerns[0])
output_DNN2=HiddenLayer(rng, input=output_DNN1.output, n_in=nkerns[0], n_out=nkerns[0])
DNN_out=debug_print(output_DNN2.output.transpose(), 'DNN_out')
U_p, W_p, b_p=create_GRU_para(rng, nkerns[0], nkerns[0])
layer_pooling_para=[U_p, W_p, b_p]
pooling=GRU_Matrix_Input(X=DNN_out, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p,W=W_p,b=b_p,bptt_truncate=-1)
translated_Q1=debug_print(pooling.output_vector_max, 'translated_Q1')
#before reasoning, do a GRU for doc: d2
U_d2, W_d2, b_d2=create_GRU_para(rng, nkerns[0], nkerns[0])
layer_d2_para=[U_d2, W_d2, b_d2]
layer_D2_GRU = GRU_Matrix_Input(X=layer_D_GRU.output_matrix, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_d2,W=W_d2,b=b_d2,bptt_truncate=-1)
#Reasoning Layer 2
repeat_Q1=debug_print(T.repeat(translated_Q1.reshape((translated_Q1.shape[0],1)), maxDocLength, axis=1)[:,:layer_D2_GRU.output_matrix.shape[1]], 'repeat_Q1')
input_DNN2=debug_print(T.concatenate([layer_D2_GRU.output_matrix,repeat_Q1], axis=0).transpose(), 'input_DNN2')#each row is an example
output_DNN3=HiddenLayer(rng, input=input_DNN2, n_in=nkerns[0]*2, n_out=nkerns[0])
output_DNN4=HiddenLayer(rng, input=output_DNN3.output, n_in=nkerns[0], n_out=nkerns[0])
DNN_out2=debug_print(output_DNN4.output.transpose(), 'DNN_out2')
U_p2, W_p2, b_p2=create_GRU_para(rng, nkerns[0], nkerns[0])
layer_pooling_para2=[U_p2, W_p2, b_p2]
pooling2=GRU_Matrix_Input(X=DNN_out2, word_dim=nkerns[0], hidden_dim=nkerns[0],U=U_p2,W=W_p2,b=b_p2,bptt_truncate=-1)
translated_Q2=debug_print(pooling2.output_vector_max, 'translated_Q2')
QA1=T.concatenate([translated_Q2, layer0_A1_output], axis=0)
QA2=T.concatenate([translated_Q2, layer0_A2_output], axis=0)
QA3=T.concatenate([translated_Q2, layer0_A3_output], axis=0)
QA4=T.concatenate([translated_Q2, layer0_A4_output], axis=0)
W_HL,b_HL=create_HiddenLayer_para(rng, n_in=nkerns[0]*2, n_out=1)
match_params=[W_HL,b_HL]
QA1_match=HiddenLayer(rng, input=QA1, n_in=nkerns[0]*2, n_out=1, W=W_HL, b=b_HL)
QA2_match=HiddenLayer(rng, input=QA2, n_in=nkerns[0]*2, n_out=1, W=W_HL, b=b_HL)
QA3_match=HiddenLayer(rng, input=QA3, n_in=nkerns[0]*2, n_out=1, W=W_HL, b=b_HL)
QA4_match=HiddenLayer(rng, input=QA4, n_in=nkerns[0]*2, n_out=1, W=W_HL, b=b_HL)
# simi_overall_level1=debug_print(cosine(translated_Q2, layer0_A1_output), 'simi_overall_level1')
# simi_overall_level2=debug_print(cosine(translated_Q2, layer0_A2_output), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(translated_Q2, layer0_A3_output), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(translated_Q2, layer0_A4_output), 'simi_overall_level4')
simi_overall_level1=debug_print(QA1_match.output[0], 'simi_overall_level1')
simi_overall_level2=debug_print(QA2_match.output[0], 'simi_overall_level2')
simi_overall_level3=debug_print(QA3_match.output[0], 'simi_overall_level3')
simi_overall_level4=debug_print(QA4_match.output[0], 'simi_overall_level4')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
#only use overall_simi
cost=T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)+T.maximum(0.0, margin+simi_overall_level3-simi_overall_level1)+T.maximum(0.0, margin+simi_overall_level4-simi_overall_level1)
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
posi_simi=simi_overall_level1
nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
# #use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_1
# nega_simi=T.max([simi_2, simi_3, simi_4])
L2_reg =debug_print((U**2).sum()+(W**2).sum()
+(U_p**2).sum()+(W_p**2).sum()
+(U_p2**2).sum()+(W_p2**2).sum()
+(U_d**2).sum()+(W_d**2).sum()
+(U_d2**2).sum()+(W_d2**2).sum()
+(output_DNN1.W**2).sum()+(output_DNN2.W**2).sum()
+(output_DNN3.W**2).sum()+(output_DNN4.W**2).sum()
+(W_HL**2).sum(), 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
}, on_unused_input='ignore')
params = layer0_para+output_DNN1.params+output_DNN2.params+output_DNN3.params+output_DNN4.params+layer_pooling_para+layer_pooling_para2+match_params+layer_d_para+layer_d2_para
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = decay*acc_i + (1-decay)*T.sqr(grad_i) #rmsprop
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-6)))
# updates.append((acc_i, acc))
def AdaDelta_updates(parameters,gradients,rho,eps):
# create variables to store intermediate updates
gradients_sq = [ theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters ]
deltas_sq = [ theano.shared(numpy.zeros(p.get_value().shape)) for p in parameters ]
# calculates the new "average" delta for the next iteration
gradients_sq_new = [ rho*g_sq + (1-rho)*(g**2) for g_sq,g in zip(gradients_sq,gradients) ]
# calculates the step in direction. The square root is an approximation to getting the RMS for the average value
deltas = [ (T.sqrt(d_sq+eps)/T.sqrt(g_sq+eps))*grad for d_sq,g_sq,grad in zip(deltas_sq,gradients_sq_new,gradients) ]
# calculates the new "average" deltas for the next step.
deltas_sq_new = [ rho*d_sq + (1-rho)*(d**2) for d_sq,d in zip(deltas_sq,deltas) ]
# Prepare it as a list f
gradient_sq_updates = zip(gradients_sq,gradients_sq_new)
deltas_sq_updates = zip(deltas_sq,deltas_sq_new)
parameters_updates = [ (p,p - d) for p,d in zip(parameters,deltas) ]
return gradient_sq_updates + deltas_sq_updates + parameters_updates
updates=AdaDelta_updates(params, grads, decay, 1e-6)
train_model = theano.function([index], [cost, posi_simi, nega_simi], updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
# shuffle(train_batch_start)#shuffle training data
corr_train=0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
sys.stdout.write( "Training :[%6f] %% complete!\r" % ((iter%train_size)*100.0/train_size) )
sys.stdout.flush()
minibatch_index=minibatch_index+1
cost_average, posi_simi, nega_simi= train_model(batch_start)
if posi_simi>nega_simi:
corr_train+=1
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+'corr rate:'+str(corr_train*100.0/train_size)
if iter % validation_frequency == 0:
corr_test=0
for i in test_batch_start:
cost, posi_simi, nega_simi=test_model(i)
if posi_simi>nega_simi:
corr_test+=1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc=corr_test*1.0/test_size
#test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better=False
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
find_better=True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc,'(at',best_epoch,')'
if find_better==True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(
learning_rate=0.001,
n_epochs=2000,
nkerns=[90,90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59,
margin=0.2
):
maxSentLength = max_s_length+2*(window_width-1)
maxDocLength = max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='../data/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size=load_MCTest_corpus_DPNQ(
rootPath+'vocab_DQAAAA.txt', # DPNQ.txt',
rootPath+'mc500.train.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt',
rootPath+'mc500.test.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt',
max_s_length,
maxSentLength,
maxDocLength
) # vocab_size contains train, dev and test
[train_data_D,
train_data_A1,
train_data_A2,
train_data_A3,
train_Label,
train_Length_D,train_Length_D_s,
train_Length_A1,
train_Length_A2,
train_Length_A3,
train_leftPad_D,train_leftPad_D_s,
train_leftPad_A1,
train_leftPad_A2,
train_leftPad_A3,
train_rightPad_D,train_rightPad_D_s,
train_rightPad_A1,
train_rightPad_A2,
train_rightPad_A3
] = train_data
[test_data_D,
test_data_A1,
test_data_A2,
test_data_A3,
test_Label,
test_Length_D,test_Length_D_s,
test_Length_A1,
test_Length_A2,
test_Length_A3,
test_leftPad_D,test_leftPad_D_s,
test_leftPad_A1,
test_leftPad_A2,
test_leftPad_A3,
test_rightPad_D,test_rightPad_D_s,
test_rightPad_A1,
test_rightPad_A2,
test_rightPad_A3
] = test_data
n_train_batches = train_size/batch_size
n_test_batches = test_size/batch_size
train_batch_start = list(numpy.arange(n_train_batches)*batch_size)
test_batch_start = list(numpy.arange(n_test_batches)*batch_size)
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
def evaluate_lenet5(learning_rate=0.008, n_epochs=2000, nkerns=[400], batch_size=1, window_width=3,
maxSentLength=30, emb_size=300, hidden_size=[300,10],
margin=0.5, L2_weight=0.0001, Div_reg=0.0001, norm_threshold=5.0, use_svm=False):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/';
rng = numpy.random.RandomState(23455)
datasets, word2id=load_msr_corpus_20161229(rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength)
vocab_size=len(word2id)+1
mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_number_matching_scores.txt', rootPath+'test_number_matching_scores.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2]
indices_train_r=indices_train[1::2]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2]
indices_test_r=indices_test[1::2]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
train_size = len(indices_train_l)
test_size = len(indices_test_l)
train_batch_start=range(train_size)
test_batch_start=range(test_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int32')
# indices_train_r=T.cast(indices_train_r, 'int32')
# indices_test_l=T.cast(indices_test_l, 'int32')
# indices_test_r=T.cast(indices_test_r, 'int32')
rand_values=random_value_normal((vocab_size, emb_size), theano.config.floatX, rng)
# rand_values[0]=numpy.array(numpy.zeros(emb_size))
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init_new(rand_values, id2word, word2vec)
embeddings=theano.shared(value=numpy.array(rand_values,dtype=theano.config.floatX), borrow=True)#theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.iscalar()
x_index_l = T.imatrix() # now, x is the index matrix, must be integer
x_index_r = T.imatrix()
y = T.ivector()
left_l=T.iscalar()
right_l=T.iscalar()
left_r=T.iscalar()
right_r=T.iscalar()
length_l=T.iscalar()
length_r=T.iscalar()
norm_length_l=T.fscalar()
norm_length_r=T.fscalar()
mts=T.fmatrix()
wmf=T.fmatrix()
# cost_tmp=T.fscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer0_l_output_maxpool = T.max(layer0_l.output_narrow_conv_out[:,:,:,left_l:], axis=3).reshape((1, nkerns[0]))
layer0_r_output_maxpool = T.max(layer0_r.output_narrow_conv_out[:,:,:,left_r:], axis=3).reshape((1, nkerns[0]))
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
sum_uni_l=T.sum(layer0_l_input[:,:,:,left_l:], axis=3).reshape((1, emb_size))
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input[:,:,:,left_r:], axis=3).reshape((1, emb_size))
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
HL_layer_1_input=T.concatenate([
# mts,
eucli_1, #uni_cosine,norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
uni_cosine,
# sum_uni_l,
# sum_uni_r,
# sum_uni_l+sum_uni_r,
1.0/(1.0+EUCLID(layer0_l_output_maxpool, layer0_r_output_maxpool)),
cosine(layer0_l_output_maxpool, layer0_r_output_maxpool),
layer0_l_output_maxpool,
layer0_r_output_maxpool,
T.sqrt((layer0_l_output_maxpool-layer0_r_output_maxpool)**2+1e-10),
layer1.output_eucli_to_simi, #layer1.output_cosine,layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
layer1.output_cosine,
layer1.output_vector_l,
layer1.output_vector_r,
T.sqrt((layer1.output_vector_l-layer1.output_vector_r)**2+1e-10),
# len_l, len_r
layer1.output_attentions
# wmf,
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
HL_layer_1_input_with_extra=T.concatenate([#HL_layer_1_input,
mts, len_l, len_r
# wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
HL_layer_1_input_size=1+1+ 1+1+3* nkerns[0] +1+1+3*nkerns[0]+10*10
HL_layer_1_input_with_extra_size = HL_layer_1_input_size+15+2
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size[0], activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size[0], n_out=hidden_size[1], activation=T.tanh)
LR_layer_input=T.concatenate([HL_layer_2.output, HL_layer_1.output, HL_layer_1_input],axis=1)
LR_layer_input_with_extra=T.concatenate([HL_layer_2.output, HL_layer_1_input_with_extra],axis=1)#HL_layer_1.output,
LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=HL_layer_1_input_size+hidden_size[0]+hidden_size[1], n_out=2)
# LR_layer_input=HL_layer_2.output
# LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=hidden_size, n_out=2)
# layer3=LogisticRegression(rng, input=layer3_input, n_in=15+1+1+2+3, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((LR_layer.W** 2).sum()+(HL_layer_2.W** 2).sum()+(HL_layer_1.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()
# diversify_reg= Diversify_Reg(LR_layer.W.T)+Diversify_Reg(HL_layer_2.W.T)+Diversify_Reg(HL_layer_1.W.T)+Diversify_Reg(conv_W_into_matrix)
cost_this =debug_print(LR_layer.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=cost_this+L2_weight*L2_reg#+Div_reg*diversify_reg
test_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [LR_layer.errors(y), LR_layer.y_pred, LR_layer_input_with_extra, y], on_unused_input='ignore',allow_input_downcast=True)
params = LR_layer.params+ HL_layer_2.params+HL_layer_1.params+[conv_W, conv_b]+[embeddings]#+[embeddings]# + layer1.params
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
clipped_grad = T.clip(grad_i, -0.5, 0.5)
acc = acc_i + T.sqr(clipped_grad)
updates.append((param_i, param_i - learning_rate * clipped_grad / T.sqrt(acc+1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [cost,LR_layer.errors(y)], updates=updates, on_unused_input='ignore',allow_input_downcast=True)
train_model_predict = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [cost_this,LR_layer.errors(y), LR_layer_input_with_extra, y],on_unused_input='ignore',allow_input_downcast=True)
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
max_acc=0.0
nn_max_acc=0.0
best_iter=0
cost_tmp=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
for index in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * train_size + minibatch_index +1
minibatch_index=minibatch_index+1
# if iter%update_freq != 0:
# cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
# #print 'cost_ij: ', cost_ij
# cost_tmp+=cost_ij
# error_sum+=error_ij
# else:
cost_i, error_i= train_model(indices_train_l[index: index + batch_size],
indices_train_r[index: index + batch_size],
trainY[index: index + batch_size],
trainLeftPad_l[index],
trainRightPad_l[index],
trainLeftPad_r[index],
trainRightPad_r[index],
trainLengths_l[index],
trainLengths_r[index],
normalized_train_length_l[index],
normalized_train_length_r[index],
mt_train[index: index + batch_size],
wm_train[index: index + batch_size])
cost_tmp+=cost_i
if iter < 6000 and iter %100 ==0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter)
if iter >= 6000 and iter % 100 == 0:
# if iter%100 ==0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter)
test_losses=[]
test_y=[]
test_features=[]
for index in test_batch_start:
test_loss, pred_y, layer3_input, y=test_model(indices_test_l[index: index + batch_size],
indices_test_r[index: index + batch_size],
testY[index: index + batch_size],
testLeftPad_l[index],
testRightPad_l[index],
testLeftPad_r[index],
testRightPad_r[index],
testLengths_l[index],
testLengths_r[index],
normalized_test_length_l[index],
normalized_test_length_r[index],
mt_test[index: index + batch_size],
wm_test[index: index + batch_size])
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc = (1-test_score) * 100.
if test_acc > nn_max_acc:
nn_max_acc = test_acc
print '\t\t\tepoch:', epoch, 'iter:', iter, 'current acc:', test_acc, 'nn_max_acc:', nn_max_acc
#now, see the results of svm
if use_svm:
train_y=[]
train_features=[]
for index in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(indices_train_l[index: index + batch_size],
indices_train_r[index: index + batch_size],
trainY[index: index + batch_size],
trainLeftPad_l[index],
trainRightPad_l[index],
trainLeftPad_r[index],
trainRightPad_r[index],
trainLengths_l[index],
trainLengths_r[index],
normalized_train_length_l[index],
normalized_train_length_r[index],
mt_train[index: index + batch_size],
wm_train[index: index + batch_size])
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(' '.join(map(str,layer3_input[0]))+'\n')
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
corr_count=0
corr_lr=0
test_size=len(test_y)
for i in range(test_size):
if results[i]==test_y[i]:
corr_count+=1
if numpy.absolute(results_lr[i]-test_y[i])<0.5:
corr_lr+=1
acc=corr_count*1.0/test_size
acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_iter=iter
if acc_lr> max_acc:
max_acc=acc_lr
best_iter=iter
print '\t\t\t\tsvm acc: ', acc, 'LR acc: ', acc_lr, ' max acc: ', max_acc , ' at iter: ', best_iter
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.085, n_epochs=2000, nkerns=[50, 50], batch_size=1, window_width=7,
maxSentLength=60, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.00005, update_freq=10, norm_threshold=5.0):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_msr_corpus(rootPath+'vocab.txt', rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength)
mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int32')
indices_train_r=T.cast(indices_train_r, 'int32')
indices_test_l=T.cast(indices_test_l, 'int32')
indices_test_r=T.cast(indices_test_r, 'int32')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.imatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.imatrix('x_index_r')
y = T.ivector('y')
left_l=T.iscalar()
right_l=T.iscalar()
left_r=T.iscalar()
right_r=T.iscalar()
length_l=T.iscalar()
length_r=T.iscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer0_para=[conv_W, conv_b]
layer1=Average_Pooling(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1, window_size=window_width, maxSentLength=maxSentLength)
conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_size[1]))
layer2_l = Conv_with_input_para(rng, input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_size[1]), W=conv2_W, b=conv2_b)
layer2_r = Conv_with_input_para(rng, input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_size[1]), W=conv2_W, b=conv2_b)
layer2_para=[conv2_W, conv2_b]
layer3=Average_Pooling_for_batch1(rng, input_l=layer2_l.output, input_r=layer2_r.output, kern=nkerns[1],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
layer3_out=debug_print(layer3.output_simi, 'layer1_out')
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
#norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
#norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
'''
uni_cosine=cosine(sum_uni_l, sum_uni_r)
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer4_input=T.concatenate([mts, eucli_1,layer1.output_eucli, layer3_out,len_l, len_r], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4=LogisticRegression(rng, input=layer4_input, n_in=15+3+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer4.W** 2).sum()+(conv2_W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()
cost_this =debug_print(layer4.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
test_model = theano.function([index], [layer4.errors(y), layer4.y_pred],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size]}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params+ layer2_para+ layer0_para# + layer1.params
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
#norm=T.sqrt((grad_i**2).sum())
#if T.lt(norm_threshold, norm):
# print 'big norm'
# grad_i=grad_i*(norm_threshold/norm)
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index], [cost,layer4.errors(y), layer4_input], updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size]}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer4.errors(y)],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
if iter%update_freq != 0:
cost_ij, error_ij=train_model_predict(batch_start)
#print 'cost_ij: ', cost_ij
cost_tmp+=cost_ij
error_sum+=error_ij
else:
cost_average, error_ij, layer3_input= train_model(batch_start)
#print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' sum error: '+str(error_sum)+'/'+str(update_freq)
error_sum=0
cost_tmp=0#reset for the next batch
#print layer3_input
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
for i in test_batch_start:
test_loss, pred_y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
#print 'validating & testing...'
# compute zero-one loss on validation set
validation_losses = []
for i in dev_batch_start:
time.sleep(0.5)
validation_losses.append(validate_model(i))
#validation_losses = [validate_model(i) for i in dev_batch_start]
this_validation_loss = numpy.mean(validation_losses)
print('\t\tepoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index , n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in test_batch_start]
test_score = numpy.mean(test_losses)
print(('\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1,
n_epochs=2000,
word_nkerns=300,
batch_size=1,
window_width=[3, 3],
emb_size=300,
margin=0.5,
L2_weight=0.0003,
Div_reg=0.03,
update_freq=1,
norm_threshold=5.0,
max_truncate=40,
max_relation_len=6,
max_Q_len=30,
neg_all=100,
train_size=69967,
test_size=19953,
mark='_RC_newdata'): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/home/wyin/Datasets/SimpleQuestions_v2/relation_classification/'
triple_files = [
'train.replace_ne.withpoolwenpengFormat.txt',
'test.replace_ne.withpoolwenpengFormat.txt'
]
rng = numpy.random.RandomState(23455)
datasets, datasets_test, length_per_example_train, length_per_example_test, vocab_size = load_train(
triple_files[0], triple_files[1], max_relation_len, max_Q_len,
train_size, test_size,
mark) #max_char_len, max_des_len, max_relation_len, max_Q_len
print 'vocab_size:', vocab_size
train_data = datasets
# valid_data=datasets[1]
test_data = datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
train_relations = train_data[0]
train_relation_lengths = train_data[1]
train_remainQ_word_ids = train_data[2]
train_remainQ_word_len = train_data[3]
test_relations = test_data[0]
test_relation_lengths = test_data[1]
test_remainQ_word_ids = test_data[2]
test_remainQ_word_len = test_data[3]
train_sizes = [
len(train_relations),
len(train_relation_lengths),
len(train_remainQ_word_ids),
len(train_remainQ_word_len)
]
if sum(train_sizes) / len(train_sizes) != train_size:
print 'weird size:', train_sizes
exit(0)
test_sizes = [
len(test_relations),
len(test_relation_lengths),
len(test_remainQ_word_ids),
len(test_remainQ_word_len)
]
if sum(test_sizes) / len(test_sizes) != test_size:
print 'weird size:', test_sizes
exit(0)
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
# indices_train_pos_entity_char=theano.shared(numpy.asarray(train_pos_entity_char, dtype='int32'), borrow=True)
# indices_train_pos_entity_des=theano.shared(numpy.asarray(train_pos_entity_des, dtype='int32'), borrow=True)
# indices_train_relations=theano.shared(numpy.asarray(train_relations, dtype='int32'), borrow=True)
# indices_train_entity_char_lengths=theano.shared(numpy.asarray(train_entity_char_lengths, dtype='int32'), borrow=True)
# indices_train_entity_des_lengths=theano.shared(numpy.asarray(train_entity_des_lengths, dtype='int32'), borrow=True)
# indices_train_relation_lengths=theano.shared(numpy.asarray(train_relation_lengths, dtype='int32'), borrow=True)
# indices_train_mention_char_ids=theano.shared(numpy.asarray(train_mention_char_ids, dtype='int32'), borrow=True)
# indices_train_remainQ_word_ids=theano.shared(numpy.asarray(train_remainQ_word_ids, dtype='int32'), borrow=True)
# indices_train_mention_char_lens=theano.shared(numpy.asarray(train_mention_char_lens, dtype='int32'), borrow=True)
# indices_train_remainQ_word_len=theano.shared(numpy.asarray(train_remainQ_word_len, dtype='int32'), borrow=True)
# indices_train_entity_scores=theano.shared(numpy.asarray(train_entity_scores, dtype=theano.config.floatX), borrow=True)
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
# rand_values=load_word2vec_to_init(rand_values, rootPath+'word_emb'+mark+'.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.iscalar()
rel_word_ids_M = T.imatrix()
rel_word_lens_M = T.imatrix()
q_word_ids_f = T.ivector()
q_word_lens_f = T.ivector()
filter_size = (emb_size, window_width[0])
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
word_filter_shape = (word_nkerns, 1, filter_size[0], filter_size[1])
q_rel_conv_W, q_rel_conv_b = create_conv_para(
rng, filter_shape=word_filter_shape)
params = [embeddings, q_rel_conv_W, q_rel_conv_b]
q_rel_conv_W_into_matrix = q_rel_conv_W.reshape(
(q_rel_conv_W.shape[0], q_rel_conv_W.shape[2] * q_rel_conv_W.shape[3]))
# load_model_from_file(rootPath, params, '')
def SimpleQ_matches_Triple(rel_word_ids_f, rel_word_lens_f):
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_rel_pool = Average_Pooling_for_SimpleQA(
rng,
input_l=q_rel_conv.output,
input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2],
length_l=q_word_lens_f[1] + filter_size[1] - 1,
dim=max_Q_len + filter_size[1] - 1,
topk=2)
overall_simi = cosine(q_rel_pool.output_maxpooling,
rel_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(
SimpleQ_matches_Triple, sequences=[rel_word_ids_M, rel_word_lens_M])
posi_simi = simi_list[0]
nega_simies = simi_list[1:]
loss_simi_list = T.maximum(
0.0, margin - posi_simi.reshape((1, 1)) + nega_simies)
loss_simi = T.sum(loss_simi_list)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(embeddings**2).sum() + (q_rel_conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg = Diversify_Reg(q_rel_conv_W_into_matrix)
cost = loss_simi + L2_weight * L2_reg + Div_reg * diversify_reg
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[rel_word_ids_M, rel_word_lens_M, q_word_ids_f, q_word_lens_f],
[loss_simi, simi_list],
on_unused_input='ignore')
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
# updates.append((acc_i, acc))
if param_i == embeddings:
updates.append(
(param_i,
T.set_subtensor(
(param_i -
learning_rate * grad_i / T.sqrt(acc + 1e-10))[0],
theano.shared(numpy.zeros(emb_size))))) #Ada
else:
updates.append(
(param_i, param_i -
learning_rate * grad_i / T.sqrt(acc + 1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[rel_word_ids_M, rel_word_lens_M, q_word_ids_f, q_word_lens_f],
[loss_simi, cost],
updates=updates,
on_unused_input='ignore')
# train_model = theano.function([index, chosed_indices], [loss_simi, cost], updates=updates,
# givens={
# rel_word_ids_M : indices_train_relations[index].reshape((neg_all, max_relation_len))[chosed_indices].reshape((train_neg_size, max_relation_len)),
# rel_word_lens_M : indices_train_relation_lengths[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
# q_word_ids_M : indices_train_remainQ_word_ids[index].reshape((neg_all, max_Q_len))[chosed_indices].reshape((train_neg_size, max_Q_len)),
# q_word_lens_M : indices_train_remainQ_word_len[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3))
#
# }, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
best_test_accu = 0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
for jj in range(train_size):
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#print batch_start
train_rel_word_ids_M = numpy.asarray(
train_relations[jj], dtype='int32').reshape(
(length_per_example_train[jj], max_relation_len))
train_rel_word_lens_M = numpy.asarray(
train_relation_lengths[jj], dtype='int32').reshape(
(length_per_example_train[jj], 3))
train_q_word_ids_M = numpy.asarray(
train_remainQ_word_ids[jj], dtype='int32'
) #.reshape((length_per_example_train[jj], max_Q_len))
train_q_word_lens_M = numpy.asarray(
train_remainQ_word_len[jj],
dtype='int32') #.reshape((length_per_example_train[jj], 3))
loss_simi_i, cost_i = train_model(train_rel_word_ids_M,
train_rel_word_lens_M,
train_q_word_ids_M,
train_q_word_lens_M)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + '\tloss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
#if iter ==1:
# exit(0)
#
if iter > 59999 and iter % 10000 == 0:
test_loss = []
succ = 0
for i in range(test_size):
# print 'testing', i, '...'
#prepare data
test_rel_word_ids_M = numpy.asarray(
test_relations[i], dtype='int32').reshape(
(length_per_example_test[i], max_relation_len))
test_rel_word_lens_M = numpy.asarray(
test_relation_lengths[i], dtype='int32').reshape(
(length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(
test_remainQ_word_ids[i], dtype='int32'
) #.reshape((length_per_example_test[i], max_Q_len))
test_q_word_lens_M = numpy.asarray(
test_remainQ_word_len[i], dtype='int32'
) #.reshape((length_per_example_test[i], 3))
loss_simi_i, simi_list_i = test_model(
test_rel_word_ids_M, test_rel_word_lens_M,
test_q_word_ids_M, test_q_word_lens_M)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if simi_list_i[0] >= max(simi_list_i[1:]):
succ += 1
# print 'testing', i, '...acc:', succ*1.0/(i+1)
succ = (succ + 20610 - test_size) * 1.0 / 20610
#now, check MAP and MRR
print((
'\t\t\t\t\t\tepoch %i, minibatch %i/%i, test accu of best '
'model %f') %
(epoch, minibatch_index, n_train_batches, succ))
if best_test_accu < succ:
best_test_accu = succ
store_model_to_file(rootPath, params, mark)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=2000,
batch_size=100,
emb_size=10,
hidden_size=10,
L2_weight=0.0001,
para_len_limit=400,
q_len_limit=40,
max_EM=0.217545454546):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist = load_train(
para_len_limit, q_len_limit)
train_size = len(train_para_list)
if train_size != len(train_Q_list) or train_size != len(
train_label_list) or train_size != len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist = load_dev_or_test(
word2id, para_len_limit, q_len_limit)
test_size = len(test_para_list)
if test_size != len(test_Q_list) or test_size != len(
test_mask) or test_size != len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values = random_value_normal((overall_vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extraF = T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
norm_extraF = normalize_matrix(extraF)
U1, W1, b1 = create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b = create_GRU_para(rng, emb_size, hidden_size)
paragraph_para = [U1, W1, b1, U1_b, W1_b, b1_b]
UQ, WQ, bQ = create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b = create_GRU_para(rng, emb_size, hidden_size)
Q_para = [UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
W_a1 = create_ensemble_para(
rng, hidden_size,
hidden_size) # init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size + 3) # 3 extra features
LR_b = theano.shared(
value=numpy.zeros((2, ),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b',
borrow=True)
attention_paras = [W_a1, W_a2, U_a, LR_b]
params = [embeddings] + paragraph_para + Q_para + attention_paras
load_model_from_file(rootPath + 'Best_Paras_conv_0.217545454545', params)
paragraph_input = embeddings[paragraph.flatten()].reshape(
(paragraph.shape[0], paragraph.shape[1], emb_size)).transpose(
(0, 2, 1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input = T.concatenate(
[paragraph_input, norm_extraF.dimshuffle((0, 2, 1))], axis=1)
paragraph_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=paragraph_input,
Mask=para_mask,
hidden_dim=hidden_size,
U=U1,
W=W1,
b=b1,
Ub=U1_b,
Wb=W1_b,
bb=b1_b)
para_reps = paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape(
(questions.shape[0], questions.shape[1], emb_size)).transpose(
(0, 2, 1)) #(#questions, emb_size, maxsenlength)
questions_model = Bd_GRU_Batch_Tensor_Input_with_Mask(
X=Qs_emb,
Mask=q_mask,
hidden_dim=hidden_size,
U=UQ,
W=WQ,
b=bQ,
Ub=UQ_b,
Wb=WQ_b,
bb=bQ_b)
# questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
questions_reps_tensor = questions_model.output_tensor
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#use CNN for question modeling
# Qs_emb_tensor4=Qs_emb.dimshuffle((0,'x', 1,2)) #(batch_size, 1, emb+3, maxparalen)
# conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, 5))
# Q_conv_para=[conv_W, conv_b]
# conv_model = Conv_with_input_para(rng, input=Qs_emb_tensor4,
# image_shape=(batch_size, 1, emb_size, q_len_limit),
# filter_shape=(hidden_size, 1, emb_size, 5), W=conv_W, b=conv_b)
# conv_output=conv_model.narrow_conv_out.reshape((batch_size, hidden_size, q_len_limit-5+1)) #(batch, 1, hidden_size, maxparalen-1)
# gru_mask=(q_mask[:,:-4]*q_mask[:,1:-3]*q_mask[:,2:-2]*q_mask[:,3:-1]*q_mask[:,4:]).reshape((batch_size, 1, q_len_limit-5+1))
# masked_conv_output=conv_output*gru_mask
# questions_conv_reps=T.max(masked_conv_output, axis=2).reshape((batch_size, 1, hidden_size))
# new_labels=T.gt(labels[:,:-1]+labels[:,1:], 0.0)
# ConvGRU_1=Conv_then_GRU_then_Classify(rng, concate_paragraph_input, Qs_emb, para_len_limit, q_len_limit, emb_size+3, hidden_size, emb_size, 2, batch_size, para_mask, q_mask, new_labels, 2)
# ConvGRU_1_dis=ConvGRU_1.masked_dis_inprediction
# padding_vec = T.zeros((batch_size, 1), dtype=theano.config.floatX)
# ConvGRU_1_dis_leftpad=T.concatenate([padding_vec, ConvGRU_1_dis], axis=1)
# ConvGRU_1_dis_rightpad=T.concatenate([ConvGRU_1_dis, padding_vec], axis=1)
# ConvGRU_1_dis_into_unigram=0.5*(ConvGRU_1_dis_leftpad+ConvGRU_1_dis_rightpad)
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix = para_matrix.T
interaction_matrix = T.dot(transpose_para_matrix,
q_matrix) #(para_len, q_len)
norm_interaction_matrix = T.nnet.softmax(interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T) #(len, para_len)
batch_q_reps, updates = theano.scan(
fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor
]) #batch_q_reps (batch, hidden, para_len)
#attention distributions
norm_W_a1 = normalize_matrix(W_a1)
norm_W_a2 = normalize_matrix(W_a2)
norm_U_a = normalize_matrix(U_a)
transformed_para_reps = T.maximum(
T.dot(para_reps.transpose((0, 2, 1)), norm_W_a2), 0.0) #relu
transformed_q_reps = T.maximum(
T.dot(batch_q_reps.transpose((0, 2, 1)), norm_W_a1), 0.0)
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both = transformed_para_reps + transformed_q_reps
# U_c, W_c, b_c=create_GRU_para(rng, hidden_size, hidden_size)
# U_c_b, W_c_b, b_c_b=create_GRU_para(rng, hidden_size, hidden_size)
# accumu_para=[U_c, W_c, b_c, U_c_b, W_c_b, b_c_b]
# accumu_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_both.transpose((0,2,1)), Mask=para_mask, hidden_dim=hidden_size,U=U_c,W=W_c,b=b_c,Ub=U_c_b,Wb=W_c_b,bb=b_c_b)
# accu_both=accumu_model.output_tensor.transpose((0,2,1))
prior_att = T.concatenate([add_both, norm_extraF], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices = para_mask.flatten().nonzero()[0]
layer3 = LogisticRegression(rng,
input=prior_att.reshape(
(batch_size * prior_att.shape[1],
hidden_size + 3)),
n_in=hidden_size + 3,
n_out=2,
W=norm_U_a,
b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.sum(
T.log(layer3.p_y_given_x)
[valid_indices,
labels.flatten()[valid_indices]]) #[T.arange(y.shape[0]), y])
distributions = layer3.p_y_given_x[:, -1].reshape(
(batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
# masked_dis=(distributions+ConvGRU_1_dis_into_unigram)*para_mask
masked_dis = distributions * para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
L2_reg = L2norm_paraList(
[embeddings, U1, W1, U1_b, W1_b, UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost = error #+ConvGRU_1.error#
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[paragraph, questions, labels, para_mask, q_mask, extraF],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function(
[paragraph, questions, para_mask, q_mask, extraF],
masked_dis,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / batch_size
# remain_test=test_size%batch_size
test_batch_start = list(
numpy.arange(n_test_batches) * batch_size) + [test_size - batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i += train_model(
np.asarray([
train_para_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_Q_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_label_list[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype='int32'),
np.asarray([
train_para_mask[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX),
np.asarray([
train_mask[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX),
np.asarray([
train_feature_matrixlist[id]
for id in train_ids[para_id:para_id + batch_size]
],
dtype=theano.config.floatX))
#print iter
if iter % 10 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match = 0.0
F1_match = 0.0
q_amount = 0
for test_para_id in test_batch_start:
distribution_matrix = test_model(
np.asarray(test_para_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id +
batch_size],
dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX),
np.asarray(
test_feature_matrixlist[test_para_id:test_para_id +
batch_size],
dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list = test_text_list[
test_para_id:test_para_id + batch_size]
para_gold_ansset_list = q_ansSet_list[
test_para_id:test_para_id + batch_size]
paralist_extra_features = test_feature_matrixlist[
test_para_id:test_para_id + batch_size]
sub_para_mask = test_para_mask[test_para_id:test_para_id +
batch_size]
para_len = len(test_para_wordlist_list[0])
if para_len != len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(
distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount += batch_size
# print q_size
# print test_para_word_list
Q_list_inword = test_Q_list_word[
test_para_id:test_para_id + batch_size]
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans = extract_ansList_attentionList(
test_para_wordlist_list[q], distribution_matrix[q],
np.asarray(paralist_extra_features[q],
dtype=theano.config.floatX),
sub_para_mask[q], Q_list_inword[q])
q_gold_ans_set = para_gold_ansset_list[q]
# print test_para_wordlist_list[q]
# print Q_list_inword[q]
# print pred_ans.encode('utf8'), q_gold_ans_set
if pred_ans in q_gold_ans_set:
exact_match += 1
F1 = MacroF1(pred_ans, q_gold_ans_set)
F1_match += F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc = F1_match / q_amount
exact_acc = exact_match / q_amount
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
if max_exact_acc > max_EM:
store_model_to_file(
rootPath + 'Best_Paras_conv_' + str(max_exact_acc),
params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01,
n_epochs=100,
batch_size=100,
emb_size=300,
char_emb_size=20,
hidden_size=10,
L2_weight=0.0001,
p_len_limit=400,
test_p_len_limit=100,
q_len_limit=20,
char_len=15,
filter_size=[5, 5, 5, 5, 5],
char_filter_size=5,
margin=0.85,
extra_size=5 + 11,
extra_emb=10,
distance=10,
distance_emb=10,
comment='add distance embs'): #extra_size=3+46+7
test_batch_size = batch_size
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = numpy.random.RandomState(23455)
word2id = {}
char2id = {}
train_Q_list, train_para_list, train_Q_mask, train_para_mask, train_Q_char_list, train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras, word2id, char2id = load_squad_cnn_rank_span_word_train(
word2id, char2id, p_len_limit, q_len_limit, char_len)
test_Q_list, test_para_list, test_Q_mask, test_para_mask, test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list = load_squad_cnn_rank_span_word_dev(
word2id, char2id, test_p_len_limit, q_len_limit, char_len)
'''
#store variables into file
'''
# train_variables = [train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras]
# test_variables =[test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list]
# with open(rootPath+'extra.3.pickle', 'wb') as f: # Python 3: open(..., 'wb')
# cPickle.dump(train_variables+test_variables, f, protocol=cPickle.HIGHEST_PROTOCOL)
# f.close()
# print 'variable stored successfully'
# exit(0)
'''
load variables from file
'''
# before_load_time = time.time()
# with open(rootPath+'extra.3.pickle', 'rb') as f: # Python 3: open(..., 'rb')
# train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_span_label_list, train_word_label_list, train_para_extras,test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, q_idlist, test_para_extras, word2id, char2id, test_para_wordlist_list = cPickle.load(f)
# f.close()
# print 'load data variables successfully, spend: ', (time.time()-before_load_time)/60.0, ' mins'
train_size = len(train_para_list)
test_size = len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask,
dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask,
dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask,
dtype=theano.config.floatX)
train_para_extras = numpy.asarray(train_para_extras,
dtype=theano.config.floatX)
train_span_label_list = numpy.asarray(train_span_label_list, dtype='int32')
train_word_label_list = numpy.asarray(train_word_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask,
dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask,
dtype=theano.config.floatX)
test_para_extras = numpy.asarray(test_para_extras,
dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX, rng)
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values = random_value_normal((char_size + 1, char_emb_size),
theano.config.floatX, rng)
char_embeddings = theano.shared(value=char_rand_values, borrow=True)
extra_rand_values = random_value_normal((extra_size, extra_emb),
theano.config.floatX, rng)
extra_embeddings = theano.shared(value=extra_rand_values, borrow=True)
distance_rand_values = random_value_normal(
(2 * distance + 1, distance_emb), theano.config.floatX, rng)
distance_embeddings = theano.shared(value=distance_rand_values,
borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
span_indices = T.ivector() #batch
word_indices = T.imatrix() #(batch, 2)
ans_indices = T.ivector() # for one batch, the length is dynamic
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
extra = T.ftensor3() #(batch, p_len, 3)
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask = T.fmatrix()
char_q_mask = T.fmatrix()
true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
extra_rep_batch = T.concatenate(
[extra.dot(extra_embeddings), extra],
axis=2) #(batch, p_len, extra_emb+extra_size)
zero_pad = T.zeros((true_batch_size, 1, extra_emb + extra_size))
left_context = T.concatenate([zero_pad, extra_rep_batch[:, :-1, :]],
axis=1) #(batch, p_len, extra_emb+extra_size)
right_context = T.concatenate(
[extra_rep_batch[:, 1:, :], zero_pad],
axis=1) #(batch, p_len, extra_emb+extra_size)
left_context_2 = T.concatenate(
[zero_pad, zero_pad, extra_rep_batch[:, :-2, :]],
axis=1) #(batch, p_len, extra_emb+extra_size)
right_context_2 = T.concatenate(
[extra_rep_batch[:, 2:, :], zero_pad, zero_pad],
axis=1) #(batch, p_len, extra_emb+extra_size)
simi2left = T.sum(extra_rep_batch * left_context,
axis=2).dimshuffle(0, 1, 'x') #(batch, p_len, 1)
simi2right = T.sum(extra_rep_batch * right_context,
axis=2).dimshuffle(0, 1, 'x') #(batch, p_len, 1)
cos2left = cosine_tensor3(extra_rep_batch, left_context,
2).dimshuffle(0, 1, 'x')
cos2right = cosine_tensor3(extra_rep_batch, right_context,
2).dimshuffle(0, 1, 'x')
diff2left = extra_rep_batch - left_context
diff2right = extra_rep_batch - right_context #(batch, p_len, extra_emb+extra_size)
extra_rep_batch = T.concatenate(
[
extra_rep_batch, left_context, right_context, left_context_2,
right_context_2, diff2left, diff2right, simi2left, simi2right,
cos2left, cos2right
],
axis=2) #batch, p_len, 7*(extra_emb+extra_size)+4)
true_extra_size = 7 * (extra_emb + extra_size) + 4
common_input_p = embeddings[paragraph.flatten()].reshape(
(true_batch_size, true_p_len,
emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q = embeddings[questions.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size))
char_common_input_p = char_embeddings[char_paragraph.flatten()].reshape(
(true_batch_size * true_p_len, char_len, char_emb_size
)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q = char_embeddings[char_questions.flatten()].reshape(
(true_batch_size * q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape(
(true_batch_size * true_p_len, char_len))
char_q_masks = char_q_mask.reshape(
(true_batch_size * q_len_limit, char_len))
conv_W_char, conv_b_char = create_conv_para(
rng, filter_shape=(char_emb_size, 1, char_emb_size, char_filter_size))
conv_W_1, conv_b_1 = create_conv_para(
rng,
filter_shape=(hidden_size, 1,
emb_size + char_emb_size + true_extra_size,
filter_size[0]))
conv_W_2, conv_b_2 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_3, conv_b_3 = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[2]))
# conv_W_4, conv_b_4=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[3]))
# conv_W_5, conv_b_5=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[4]))
conv_W_1_q, conv_b_1_q = create_conv_para(
rng,
filter_shape=(hidden_size, 1, emb_size + char_emb_size,
filter_size[0]))
conv_W_2_q, conv_b_2_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[1]))
conv_W_3_q, conv_b_3_q = create_conv_para(rng,
filter_shape=(hidden_size, 1,
hidden_size,
filter_size[2]))
# conv_W_4_q, conv_b_4_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[3]))
# conv_W_5_q, conv_b_5_q=create_conv_para(rng, filter_shape=(hidden_size, 1, hidden_size, filter_size[4]))
CNN_para = [
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_5, conv_b_5,conv_W_4_q, conv_b_4_q, conv_W_5_q, conv_b_5_q,
conv_W_char,
conv_b_char
]
span_input4score, word_input4score, overall_span_hidden_size, overall_word_hidden_size = squad_cnn_rank_spans_word(
rng,
common_input_p,
common_input_q,
char_common_input_p,
char_common_input_q,
batch_size,
p_len_limit,
q_len_limit,
emb_size,
char_emb_size,
char_len,
filter_size,
char_filter_size,
hidden_size,
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_char,
conv_b_char,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_4_q, conv_b_4_q,
# conv_W_5, conv_b_5, conv_W_5_q, conv_b_5_q,
para_mask,
q_mask,
char_p_masks,
char_q_masks,
extra_rep_batch,
true_extra_size)
test_span_input4score, test_word_input4score, _, _ = squad_cnn_rank_spans_word(
rng,
common_input_p,
common_input_q,
char_common_input_p,
char_common_input_q,
test_batch_size,
test_p_len_limit,
q_len_limit,
emb_size,
char_emb_size,
char_len,
filter_size,
char_filter_size,
hidden_size,
conv_W_1,
conv_b_1,
conv_W_2,
conv_b_2,
conv_W_1_q,
conv_b_1_q,
conv_W_2_q,
conv_b_2_q,
conv_W_char,
conv_b_char,
conv_W_3,
conv_b_3,
conv_W_3_q,
conv_b_3_q,
# conv_W_4, conv_b_4, conv_W_4_q, conv_b_4_q,
# conv_W_5, conv_b_5, conv_W_5_q, conv_b_5_q,
para_mask,
q_mask,
char_p_masks,
char_q_masks,
extra_rep_batch,
true_extra_size) #(batch, hidden, gram_size)
gram_size = 5 * true_p_len - (0 + 1 + 2 + 3 + 4)
# U_a = create_ensemble_para(rng, 1, 4*hidden_size)
# norm_U_a=normalize_matrix(U_a)
# span_scores_matrix=T.dot(span_input4score.dimshuffle(0,2,1), norm_U_a).reshape((batch_size, gram_size)) #(batch, 13*para_len-78, 1)
span_HL_1_para = create_ensemble_para(rng, hidden_size,
overall_span_hidden_size)
span_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
span_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_span_hidden_size)
norm_span_U_a = normalize_matrix(span_U_a)
norm_span_HL_1_para = normalize_matrix(span_HL_1_para)
norm_span_HL_2_para = normalize_matrix(span_HL_2_para)
norm_span_HL_3_para = normalize_matrix(span_HL_3_para)
norm_span_HL_4_para = normalize_matrix(span_HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(span_input4score,
norm_span_HL_1_para,
norm_span_HL_2_para,
norm_span_HL_3_para,
norm_span_HL_4_para, norm_span_U_a,
batch_size, gram_size)
span_scores = T.nnet.softmax(span_scores_matrix) #(batch, 7*para_len-21)
loss_neg_likelihood = -T.mean(
T.log(span_scores[T.arange(batch_size), span_indices]))
#ranking loss
tanh_span_scores_matrix = span_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((batch_size, gram_size), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(
index_matrix[T.arange(batch_size), span_indices], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0 -
new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi,
prob_batch_nega.shape[0],
axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x', 0),
prob_batch_posi.shape[0],
axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin - repeat_posi + repeat_nega))
span_loss = loss_neg_likelihood + loss_rank
# test_span_scores_matrix=T.dot(test_span_input4score.dimshuffle(0,2,1), norm_U_a).reshape((true_batch_size, gram_size)) #(batch, 13*para_len-78)
test_span_scores_matrix = add_HLs_2_tensor3(
test_span_input4score, norm_span_HL_1_para, norm_span_HL_2_para,
norm_span_HL_3_para, norm_span_HL_4_para, norm_span_U_a,
true_batch_size, gram_size)
#word
HL_1_para = create_ensemble_para(rng, hidden_size,
overall_word_hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
start_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_word_hidden_size)
norm_start_U_a = normalize_matrix(start_U_a)
norm_HL_1_para = normalize_matrix(HL_1_para)
norm_HL_2_para = normalize_matrix(HL_2_para)
norm_HL_3_para = normalize_matrix(HL_3_para)
norm_HL_4_para = normalize_matrix(HL_4_para)
end_HL_1_para = create_ensemble_para(
rng, hidden_size, overall_word_hidden_size + distance_emb)
end_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
end_U_a = create_ensemble_para(
rng, 1, hidden_size + overall_word_hidden_size + distance_emb)
end_norm_U_a = normalize_matrix(end_U_a)
end_norm_HL_1_para = normalize_matrix(end_HL_1_para)
end_norm_HL_2_para = normalize_matrix(end_HL_2_para)
end_norm_HL_3_para = normalize_matrix(end_HL_3_para)
end_norm_HL_4_para = normalize_matrix(end_HL_4_para)
start_scores_matrix = add_HLs_2_tensor3(word_input4score, norm_HL_1_para,
norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_start_U_a,
batch_size, true_p_len)
start_scores = T.nnet.softmax(start_scores_matrix) #(batch, para_len)
'''
forward start info to end prediction
'''
distance_matrix = word_indices[:, 0].dimshuffle(
0, 'x') - T.arange(true_p_len).dimshuffle('x', 0) #(batch, p_len)
distance_trunc_matrix = T.maximum(
-distance, T.minimum(distance,
distance_matrix)) + distance #(batch, p_len)
zero_distance_matrix = T.zeros(
(true_batch_size * true_p_len, 2 * distance + 1))
filled_distance_matrix = T.set_subtensor(
zero_distance_matrix[T.arange(true_batch_size * true_p_len),
distance_trunc_matrix.flatten()], 1.0)
filled_distance_tensor3 = filled_distance_matrix.reshape(
(true_batch_size,
true_p_len, 2 * distance + 1)).dot(distance_embeddings).dimshuffle(
0, 2, 1) #(batch_size, distance_emb, p_len)
end_word_input4score = T.concatenate(
[word_input4score, filled_distance_tensor3],
axis=1) #(batch, +distance_emb, p_len)
end_scores_matrix = add_HLs_2_tensor3(end_word_input4score,
end_norm_HL_1_para,
end_norm_HL_2_para,
end_norm_HL_3_para,
end_norm_HL_4_para, end_norm_U_a,
batch_size, true_p_len)
end_scores = T.nnet.softmax(end_scores_matrix) #(batch, para_len)
start_loss_neg_likelihood = -T.mean(
T.log(start_scores[T.arange(batch_size), word_indices[:, 0]]))
end_loss_neg_likelihood = -T.mean(
T.log(end_scores[T.arange(batch_size), word_indices[:, 1]]))
#ranking loss start
tanh_start_scores_matrix = start_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
start_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
start_new_index_matrix = T.set_subtensor(
start_index_matrix[T.arange(batch_size), word_indices[:, 0]], 1.0)
start_prob_batch_posi = tanh_start_scores_matrix[
start_new_index_matrix.nonzero()]
start_prob_batch_nega = tanh_start_scores_matrix[(
1.0 - start_new_index_matrix).nonzero()]
start_repeat_posi = T.extra_ops.repeat(start_prob_batch_posi,
start_prob_batch_nega.shape[0],
axis=0)
start_repeat_nega = T.extra_ops.repeat(start_prob_batch_nega.dimshuffle(
'x', 0),
start_prob_batch_posi.shape[0],
axis=0).flatten()
start_loss_rank = T.mean(
T.maximum(0.0, margin - start_repeat_posi + start_repeat_nega))
#ranking loss END
end_tanh_scores_matrix = end_scores #T.tanh(span_scores_matrix) #(batch, gram_size)
end_index_matrix = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX)
end_new_index_matrix = T.set_subtensor(
end_index_matrix[T.arange(batch_size), word_indices[:, 1]], 1.0)
end_prob_batch_posi = end_tanh_scores_matrix[
end_new_index_matrix.nonzero()]
end_prob_batch_nega = end_tanh_scores_matrix[(
1.0 - end_new_index_matrix).nonzero()]
end_repeat_posi = T.extra_ops.repeat(end_prob_batch_posi,
end_prob_batch_nega.shape[0],
axis=0)
end_repeat_nega = T.extra_ops.repeat(end_prob_batch_nega.dimshuffle(
'x', 0),
end_prob_batch_posi.shape[0],
axis=0).flatten()
end_loss_rank = T.mean(
T.maximum(0.0, margin - end_repeat_posi + end_repeat_nega))
word_loss = start_loss_neg_likelihood + end_loss_neg_likelihood + start_loss_rank + end_loss_rank
#test
test_start_scores_matrix = add_HLs_2_tensor3(
test_word_input4score, norm_HL_1_para, norm_HL_2_para, norm_HL_3_para,
norm_HL_4_para, norm_start_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
mask_test_start_return = test_start_scores_matrix * para_mask #(batch, p_len)
'''
forward start info to end prediction in testing
'''
test_distance_matrix = T.argmax(mask_test_start_return, axis=1).dimshuffle(
0, 'x') - T.arange(true_p_len).dimshuffle('x', 0) #(batch, p_len)
test_distance_trunc_matrix = T.maximum(
-distance, T.minimum(distance,
test_distance_matrix)) + distance #(batch, p_len)
test_zero_distance_matrix = T.zeros(
(true_batch_size * true_p_len, 2 * distance + 1))
test_filled_distance_matrix = T.set_subtensor(
test_zero_distance_matrix[T.arange(true_batch_size * true_p_len),
test_distance_trunc_matrix.flatten()], 1.0)
test_filled_distance_tensor3 = test_filled_distance_matrix.reshape(
(true_batch_size,
true_p_len, 2 * distance + 1)).dot(distance_embeddings).dimshuffle(
0, 2, 1) #(batch_size, distance_emb, p_len)
test_end_word_input4score = T.concatenate(
[test_word_input4score, test_filled_distance_tensor3],
axis=1) #(batch, +distance-emb, p_len)
end_test_scores_matrix = add_HLs_2_tensor3(
test_end_word_input4score, end_norm_HL_1_para, end_norm_HL_2_para,
end_norm_HL_3_para, end_norm_HL_4_para, end_norm_U_a, true_batch_size,
true_p_len) #(batch, test_p_len)
end_mask_test_return = end_test_scores_matrix * para_mask #(batch, p_len)
word_gram_1 = mask_test_start_return + end_mask_test_return
word_gram_2 = mask_test_start_return[:, :
-1] + end_mask_test_return[:,
1:] #(batch* hidden_size, maxsenlen-1)
word_gram_3 = mask_test_start_return[:, :
-2] + end_mask_test_return[:,
2:] #(batch* hidden_size, maxsenlen-2)
word_gram_4 = mask_test_start_return[:, :
-3] + end_mask_test_return[:,
3:] #(batch* hidden_size, maxsenlen-3)
word_gram_5 = mask_test_start_return[:, :
-4] + end_mask_test_return[:,
4:] #(batch* hidden_size, maxsenlen-4)
word_pair_scores = T.concatenate(
[word_gram_1, word_gram_2, word_gram_3, word_gram_4, word_gram_5],
axis=1) #(batch_size, gram_size)
#ans words train
ans_HL_1_para = create_ensemble_para(rng, hidden_size,
overall_word_hidden_size)
ans_HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
ans_U_a = create_ensemble_para(rng, 1,
hidden_size + overall_word_hidden_size)
norm_ans_U_a = normalize_matrix(ans_U_a)
norm_ans_HL_1_para = normalize_matrix(ans_HL_1_para)
norm_ans_HL_2_para = normalize_matrix(ans_HL_2_para)
norm_ans_HL_3_para = normalize_matrix(ans_HL_3_para)
norm_ans_HL_4_para = normalize_matrix(ans_HL_4_para)
ans_scores_matrix = add_HLs_2_tensor3(word_input4score, norm_ans_HL_1_para,
norm_ans_HL_2_para,
norm_ans_HL_3_para,
norm_ans_HL_4_para, norm_ans_U_a,
batch_size, true_p_len)
ans_scores_vec = T.nnet.softmax(
ans_scores_matrix).flatten() #(batch, para_len)
ans_loss_neg_likelihood = -T.mean(T.log(ans_scores_vec[ans_indices]))
ans_index_vec = T.zeros((batch_size, p_len_limit),
dtype=theano.config.floatX).flatten()
ans_new_index = T.set_subtensor(ans_index_vec[ans_indices], 1.0)
ans_prob_batch_posi = ans_scores_vec[ans_new_index.nonzero()]
ans_prob_batch_nega = ans_scores_vec[(1.0 - ans_new_index).nonzero()]
ans_repeat_posi = T.extra_ops.repeat(ans_prob_batch_posi,
ans_prob_batch_nega.shape[0],
axis=0)
ans_repeat_nega = T.extra_ops.repeat(ans_prob_batch_nega.dimshuffle(
'x', 0),
ans_prob_batch_posi.shape[0],
axis=0).flatten()
ans_loss_rank = T.mean(
T.maximum(0.0, margin - ans_repeat_posi + ans_repeat_nega))
ans_loss = ans_loss_neg_likelihood + ans_loss_rank
#ans words test
test_ans_scores_matrix = add_HLs_2_tensor3(
test_word_input4score, norm_ans_HL_1_para, norm_ans_HL_2_para,
norm_ans_HL_3_para, norm_ans_HL_4_para, norm_ans_U_a, true_batch_size,
true_p_len)
test_ans_scores_matrix = test_ans_scores_matrix * para_mask #T.nnet.softmax(test_ans_scores_matrix) #(batch, para_len)
ans_gram_1 = test_ans_scores_matrix
ans_gram_2 = (test_ans_scores_matrix[:, :-1] +
test_ans_scores_matrix[:, 1:]
) / 2.0 #(batch* hidden_size, maxsenlen-1)
ans_gram_3 = (test_ans_scores_matrix[:, :-2] +
test_ans_scores_matrix[:, 1:-1] +
test_ans_scores_matrix[:, 2:]
) / 3.0 #(batch* hidden_size, maxsenlen-2)
ans_gram_4 = (
test_ans_scores_matrix[:, :-3] + test_ans_scores_matrix[:, 1:-2] +
test_ans_scores_matrix[:, 2:-1] + test_ans_scores_matrix[:, 3:]
) / 4.0 #(batch* hidden_size, maxsenlen-3)
ans_gram_5 = (
test_ans_scores_matrix[:, :-4] + test_ans_scores_matrix[:, 1:-3] +
test_ans_scores_matrix[:, 2:-2] + test_ans_scores_matrix[:, 3:-1] +
test_ans_scores_matrix[:,
4:]) / 5.0 #(batch* hidden_size, maxsenlen-4)
ans_word_scores = T.concatenate(
[ans_gram_1, ans_gram_2, ans_gram_3, ans_gram_4, ans_gram_5],
axis=1) #(batch, hidden_size, maxsenlen-(0+1+2+3+4))
'''
form test spans and masks
'''
test_span_word_scores_matrix = word_pair_scores + ans_word_scores #test_span_scores_matrix+
test_spans_mask_1 = para_mask
test_spans_mask_2 = para_mask[:, :
-1] * para_mask[:,
1:] #(batch* hidden_size, maxsenlen-1)
test_spans_mask_3 = para_mask[:, :
-2] * para_mask[:, 1:
-1] * para_mask[:,
2:] #(batch* hidden_size, maxsenlen-2)
test_spans_mask_4 = para_mask[:, :
-3] * para_mask[:, 1:
-2] * para_mask[:, 2:
-1] * para_mask[:,
3:] #(batch* hidden_size, maxsenlen-3)
test_spans_mask_5 = para_mask[:, :
-4] * para_mask[:, 1:
-3] * para_mask[:, 2:
-2] * para_mask[:,
3:
-1] * para_mask[:,
4:]
test_spans_mask = T.concatenate([
test_spans_mask_1, test_spans_mask_2, test_spans_mask_3,
test_spans_mask_4, test_spans_mask_5
],
axis=1) #(batch, 5*p_len -)
# test_return=T.argmax(test_span_word_scores_matrix, axis=1) #batch T.argmax(test_span_word_scores_matrix*test_spans_mask, axis=1) #batch
test_return = T.argmax(test_span_word_scores_matrix * test_spans_mask,
axis=1) #batch
# params = [embeddings,char_embeddings]+NN_para+[U_a]
params = (
[embeddings, char_embeddings, extra_embeddings, distance_embeddings] +
CNN_para
# +[span_U_a,span_HL_1_para,span_HL_2_para,span_HL_3_para,span_HL_4_para]
+ [start_U_a, HL_1_para, HL_2_para, HL_3_para, HL_4_para] +
[end_U_a, end_HL_1_para, end_HL_2_para, end_HL_3_para, end_HL_4_para] +
[ans_U_a, ans_HL_1_para, ans_HL_2_para, ans_HL_3_para, ans_HL_4_para])
L2_reg = L2norm_paraList([
embeddings,
char_embeddings,
extra_embeddings,
distance_embeddings,
conv_W_1,
conv_W_2,
conv_W_1_q,
conv_W_2_q,
conv_W_char,
conv_W_3,
conv_W_3_q,
# conv_W_4, conv_W_5,conv_W_4_q, conv_W_5_q,
# span_U_a,span_HL_1_para,span_HL_2_para,span_HL_3_para,span_HL_4_para,
start_U_a,
HL_1_para,
HL_2_para,
HL_3_para,
HL_4_para,
end_U_a,
end_HL_1_para,
end_HL_2_para,
end_HL_3_para,
end_HL_4_para,
ans_U_a,
ans_HL_1_para,
ans_HL_2_para,
ans_HL_3_para,
ans_HL_4_para
])
#L2_reg = L2norm_paraList(params)
cost = word_loss + ans_loss + L2_weight * L2_reg #span_loss+
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([
paragraph, questions, span_indices, word_indices, ans_indices,
para_mask, q_mask, extra, char_paragraph, char_questions,
char_para_mask, char_q_mask, true_p_len
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, questions, para_mask, q_mask, extra, char_paragraph,
char_questions, char_para_mask, char_q_mask, true_p_len
],
test_return,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(numpy.arange(n_train_batches) *
batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
# remain_test=test_size%batch_size
test_batch_start = list(numpy.arange(n_test_batches) *
test_batch_size) + [test_size - test_batch_size]
max_F1_acc = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.Random(200).shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_ids[para_id:para_id + batch_size]
boundary_labels_batch = train_word_label_list[train_id_batch]
ans_label_list = []
for i in range(batch_size):
start = boundary_labels_batch[i][0] + i * p_len_limit
end = boundary_labels_batch[i][1] + i * p_len_limit
ans_label_list += range(start, end + 1)
ans_label_list = numpy.asarray(ans_label_list, dtype='int32')
cost_i += train_model(
train_para_list[train_id_batch], train_Q_list[train_id_batch],
train_span_label_list[train_id_batch], boundary_labels_batch,
ans_label_list, train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_extras[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch], p_len_limit)
#print iter
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
print 'Testing...'
past_time = time.time()
pred_dict = {}
q_amount = 0
for test_para_id in test_batch_start:
batch_predict_ids = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size],
test_Q_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_mask[test_para_id:test_para_id +
test_batch_size],
test_para_extras[test_para_id:test_para_id +
test_batch_size],
test_para_char_list[test_para_id:test_para_id +
test_batch_size],
test_Q_char_list[test_para_id:test_para_id +
test_batch_size],
test_para_char_mask[test_para_id:test_para_id +
test_batch_size],
test_Q_char_mask[test_para_id:test_para_id +
test_batch_size], test_p_len_limit)
test_para_wordlist_batch = test_para_wordlist_list[
test_para_id:test_para_id + test_batch_size]
q_ids_batch = q_idlist[test_para_id:test_para_id +
test_batch_size]
q_amount += test_batch_size
for q in range(test_batch_size): #for each question
pred_ans = decode_predict_id(
batch_predict_ids[q], test_para_wordlist_batch[q])
q_id = q_ids_batch[q]
pred_dict[q_id] = pred_ans
# print q_id, test_para_wordlist_batch[q],'\t',pred_ans
with codecs.open(rootPath + 'predictions.txt', 'w',
'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath + 'dev-v1.1.json',
rootPath + 'predictions.txt')
if F1_acc > max_F1_acc:
max_F1_acc = F1_acc
if exact_acc > max_exact_acc:
max_exact_acc = exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_exact_acc
def evaluate_lenet5(learning_rate=0.01,
n_epochs=2000,
batch_size=300,
test_batch_size=10000,
emb_size=50,
hidden_size=50,
L2_weight=0.0001,
para_len_limit=70,
q_len_limit=20,
pred_q_len_limit=50,
top_n_Qwords=1):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SQuAD/'
rng = np.random.RandomState(23455)
word2id = {}
train_para_list, train_para_mask, train_Q_list, train_Q_mask, train_start_list, train_end_list, _, word2id = load_QGQA(
word2id, para_len_limit, q_len_limit, top_n_Qwords, True)
train_size = len(train_para_list)
if train_size != len(train_Q_list) or train_size != len(
train_start_list) or train_size != len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_para_mask, test_Q_list, test_Q_mask, test_start_list, test_end_list, _, word2id = load_QGQA(
word2id, para_len_limit, q_len_limit, top_n_Qwords, False)
test_size = len(test_para_list)
train_para_list = np.asarray(train_para_list, dtype='int32')
train_para_mask = np.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_list = np.asarray(train_Q_list, dtype='int32')
train_Q_mask = np.asarray(train_Q_mask, dtype=theano.config.floatX)
train_start_list = np.asarray(train_start_list, dtype='int32')
train_end_list = np.asarray(train_end_list, dtype='int32')
test_para_list = np.asarray(test_para_list, dtype='int32')
test_para_mask = np.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_list = np.asarray(test_Q_list, dtype='int32')
test_Q_mask = np.asarray(test_Q_mask, dtype=theano.config.floatX)
test_start_list = np.asarray(test_start_list, dtype='int32')
test_end_list = np.asarray(test_end_list, dtype='int32')
vocab_size = len(word2id) + 1
# shared_decoder_mask = [0]*vocab_size
# shared_decoder_mask[0]=1#we need this pad token in generated text
# for id in train_top_Q_wordids:
# shared_decoder_mask[id]=1
# shared_decoder_mask=theano.shared(value=np.asarray(shared_decoder_mask, dtype='int32'), borrow=True) #
rand_values = random_value_normal((vocab_size, emb_size),
theano.config.floatX,
np.random.RandomState(1234))
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_glove()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings = theano.shared(value=rand_values, borrow=True)
train_top_Q_wordids = set()
wh_words = [
'What', 'Which', 'Where', 'When', 'Who', 'Whom', 'Whose', 'Why', 'How',
'far', 'many', 'much', 'long'
]
for word in wh_words:
idd = word2id.get(word)
if idd is not None:
train_top_Q_wordids.add(idd)
iddd = word2id.get(word.lower())
if iddd is not None:
train_top_Q_wordids.add(iddd)
paragraph = T.imatrix('paragraph')
questions_encoderIDs = T.imatrix() # is ground truth,
questions_decoderIDS = T.imatrix(
) #note we convert then from encoder vocab id to decoder vocab id
decoder_vocab = T.ivector()
decoder_mask = T.fmatrix() #(batch, decoder_vocab_size)
start_indices = T.ivector() #batch
end_indices = T.ivector() #batch
para_mask = T.fmatrix('para_mask')
q_mask = T.fmatrix('q_mask')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
paragraph_input = embeddings[paragraph.flatten()].reshape(
(true_batch_size, para_len_limit,
emb_size)).dimshuffle(0, 2, 1) #(batch, emb_size, para_len)
q_input = embeddings[questions_encoderIDs.flatten()].reshape(
(true_batch_size, q_len_limit, emb_size)).dimshuffle(0, 2, 1)
decoder_vocab_embs = embeddings[decoder_vocab]
fwd_LSTM_para_dict = create_LSTM_para(rng, emb_size, hidden_size)
bwd_LSTM_para_dict = create_LSTM_para(rng, emb_size, hidden_size)
paragraph_para = fwd_LSTM_para_dict.values() + bwd_LSTM_para_dict.values(
) # .values returns a list of parameters
paragraph_model = Bd_LSTM_Batch_Tensor_Input_with_Mask_Concate(
paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict,
bwd_LSTM_para_dict)
paragraph_reps_tensor3 = paragraph_model.output_tensor #(batch, 2*hidden, paralen)
batch_ids = T.arange(true_batch_size)
ans_heads = paragraph_reps_tensor3[batch_ids, :, start_indices]
ans_tails = paragraph_reps_tensor3[batch_ids, :, end_indices]
l_context_heads = paragraph_reps_tensor3[:, :, 0]
l_context_tails = paragraph_reps_tensor3[batch_ids, :, start_indices - 1]
r_context_heads = paragraph_reps_tensor3[batch_ids, :, end_indices + 1]
r_context_tails = paragraph_reps_tensor3[:, :, -1]
encoder_reps = T.concatenate([
l_context_heads, l_context_tails, ans_heads, ans_tails,
r_context_heads, r_context_tails
],
axis=1) #(batch, 6*2hidden_size)
decoder_para_dict = create_LSTM_para(rng, emb_size + 12 * hidden_size,
emb_size)
attention_para_dict1 = create_LSTM_para(rng, 2 * hidden_size, hidden_size)
attention_para_dict2 = create_LSTM_para(rng, 2 * hidden_size, hidden_size)
'''
train
'''
groundtruth_as_input = T.concatenate([
T.alloc(np.asarray(0., dtype=theano.config.floatX), true_batch_size,
emb_size, 1), q_input[:, :, :-1]
],
axis=2)
# decoder = LSTM_Decoder_Train_with_Mask(groundtruth_as_input, encoder_reps, decoder_vocab_embs, q_mask, emb_size, decoder_para_dict)
#X, Encoder_Tensor_Rep, Encoder_Mask, start_indices, end_indices, vocab_embs, Mask, emb_size, hidden_size, tparams, attention_para_dict1, attention_para_dict2
decoder = LSTM_Decoder_Train_with_Attention(
groundtruth_as_input, paragraph_reps_tensor3, para_mask, start_indices,
end_indices, decoder_vocab_embs, q_mask, emb_size, hidden_size,
decoder_para_dict, attention_para_dict1, attention_para_dict2)
prob_matrix = decoder.prob_matrix #(batch*senlen, decoder_vocab_size)
probs = prob_matrix[T.arange(true_batch_size * q_len_limit),
questions_decoderIDS.flatten()]
mask_probs = probs[(q_mask.flatten()).nonzero()]
#we shift question word ids so that in current step, the prob of previsouly predicted id gets lower and lower
shifted_question_ids = T.concatenate([
T.alloc(np.asarray(0, dtype='int32'), true_batch_size, 1),
questions_decoderIDS[:, :-1]
],
axis=1)
probs_to_minimize = prob_matrix[T.arange(true_batch_size * q_len_limit),
shifted_question_ids.flatten()]
mask_probs_to_minimize = probs_to_minimize[(q_mask.flatten()).nonzero()]
#loss train
loss = -T.mean(T.log(mask_probs)) + T.mean(T.exp(mask_probs_to_minimize))
cost = loss #+ConvGRU_1.error#
params = [embeddings] + paragraph_para + decoder_para_dict.values(
) + attention_para_dict1.values() + attention_para_dict2.values()
accumulator = []
for para_i in params:
eps_p = np.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-8))) #AdaGrad
updates.append((acc_i, acc))
# #test decoder mask
# raw_masks = T.zeros((true_batch_size, vocab_size), dtype='int32')
# x_axis = T.repeat(T.arange(true_batch_size).dimshuffle(0,'x'), paragraph.shape[1], axis=1)
# input_specific_masks = T.set_subtensor(raw_masks[x_axis.flatten(),paragraph.flatten()],1)
# overall_test_decoder_mask = T.or_(input_specific_masks, shared_decoder_mask.dimshuffle('x',0)) #(batch, vocab_size)
# overall_test_decoder_mask=(1.0-overall_test_decoder_mask)*(overall_test_decoder_mask-10)
'''
testing
'''
# test_decoder = LSTM_Decoder_Test_with_Mask(q_len_limit, encoder_reps, decoder_vocab_embs, emb_size, decoder_para_dict)
#nsteps, Encoder_Tensor_Rep, Encoder_Mask, start_indices, end_indices, vocab_embs, emb_size,hidden_size, tparams,attention_para_dict1, attention_para_dict2
test_decoder = LSTM_Decoder_Test_with_Attention(
pred_q_len_limit, paragraph_reps_tensor3, para_mask, start_indices,
end_indices, decoder_vocab_embs, decoder_mask, emb_size, hidden_size,
decoder_para_dict, attention_para_dict1, attention_para_dict2)
predictions = test_decoder.output_id_matrix #(batch, q_len_limit)
train_model = theano.function([
paragraph, questions_encoderIDs, questions_decoderIDS, decoder_vocab,
start_indices, end_indices, para_mask, q_mask
],
cost,
updates=updates,
on_unused_input='ignore')
test_model = theano.function([
paragraph, decoder_vocab, decoder_mask, start_indices, end_indices,
para_mask
],
predictions,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches = train_size / batch_size
# remain_train=train_size%batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
n_test_batches = test_size / test_batch_size
remain_test = test_size % test_batch_size
test_batch_start = list(np.arange(n_test_batches) *
test_batch_size) + [test_size - remain_test]
max_bleuscore = 0.0
max_exact_acc = 0.0
cost_i = 0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu = 0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
sub_Qs = train_Q_list[para_id:para_id + batch_size]
decoder_vocab_set = train_top_Q_wordids | set(
list(np.unique(sub_Qs)))
decoder_vocab_batch = sorted(
decoder_vocab_set) # a list of ids in order
map_encoderid2decoderid = {}
for encoderID in decoder_vocab_set:
decoderID = decoder_vocab_batch.index(encoderID)
map_encoderid2decoderid[encoderID] = decoderID
Decoder_train_Q_list = []
for id in sub_Qs.flatten():
Decoder_train_Q_list.append(map_encoderid2decoderid.get(id))
Decoder_train_Q_list = np.asarray(Decoder_train_Q_list,
dtype='int32').reshape(
(batch_size,
sub_Qs.shape[1]))
decoder_vocab_batch = np.asarray(decoder_vocab_batch,
dtype='int32')
cost_i += train_model(
train_para_list[para_id:para_id + batch_size],
train_Q_list[para_id:para_id + batch_size],
Decoder_train_Q_list, decoder_vocab_batch,
train_start_list[para_id:para_id + batch_size],
train_end_list[para_id:para_id + batch_size],
train_para_mask[para_id:para_id + batch_size],
train_Q_mask[para_id:para_id + batch_size])
#print iter
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
# print 'Testing...'
past_time = time.time()
outputfile = codecs.open('output.txt', 'w', 'utf-8')
referencefile = codecs.open('reference.txt', 'w', 'utf-8')
bleu_scores = []
for idd, test_para_id in enumerate(test_batch_start):
sub_Qs = test_Q_list[test_para_id:test_para_id +
test_batch_size]
decoder_vocab_set = train_top_Q_wordids | set(
list(np.unique(sub_Qs)))
decoder_vocab_batch = sorted(
decoder_vocab_set) # a list of ids in order
map_decoderid2encoderid = {}
for encoderID in decoder_vocab_set:
decoderID = decoder_vocab_batch.index(encoderID)
map_decoderid2encoderid[decoderID] = encoderID
if idd == len(test_batch_start) - 1:
true_test_batch_size = remain_test
else:
true_test_batch_size = test_batch_size
#decoder mask batch
decoder_mask_batch = []
for i in range(true_test_batch_size):
Q_i = test_Q_list[test_para_id + i]
decoder_vocab_Q = train_top_Q_wordids | set(
list(np.unique(Q_i)))
decoder_mask_ind = []
for ele in decoder_vocab_batch:
if ele in decoder_vocab_Q:
decoder_mask_ind.append(1.0)
else:
decoder_mask_ind.append(0.0)
decoder_mask_batch.append(decoder_mask_ind)
decoder_mask_batch = np.asarray(decoder_mask_batch,
dtype=theano.config.floatX)
decoder_vocab_batch = np.asarray(decoder_vocab_batch,
dtype='int32')
pred_id_in_batch = test_model(
test_para_list[test_para_id:test_para_id +
test_batch_size], decoder_vocab_batch,
decoder_mask_batch,
test_start_list[test_para_id:test_para_id +
test_batch_size],
test_end_list[test_para_id:test_para_id +
test_batch_size],
test_para_mask[test_para_id:test_para_id +
test_batch_size]) #(batch, senlen)
ground_truths = sub_Qs
ground_mask = test_Q_mask[test_para_id:test_para_id +
test_batch_size]
back_pred_id_in_batch = [
map_decoderid2encoderid.get(id)
for id in pred_id_in_batch.flatten()
]
for i in range(true_test_batch_size):
# print 'pred_id_in_batch[i]:', pred_id_in_batch[i]
refined_preds, refined_g = refine_decoder_predictions(
back_pred_id_in_batch[i *
pred_q_len_limit:(i + 1) *
pred_q_len_limit],
ground_truths[i], ground_mask[i])
# bleu_i = nltk.translate.bleu_score.sentence_bleu([refined_g], refined_preds)
# bleu_scores.append(bleu_i)
pred_q = ''
prev_w = ''
for id in refined_preds:
word = id2word.get(id)
if word.isalnum():
if word != prev_w:
pred_q += ' ' + word
prev_w = word
outputfile.write(pred_q + ' ?\n')
referencefile.write(
' '.join([id2word.get(id)
for id in refined_g]) + '\n')
# bleuscore = np.average(np.array(bleu_scores))
outputfile.close()
referencefile.close()
system('perl multi-bleu.perl reference.txt < output.txt')
# if max_bleuscore < bleuscore:
# max_bleuscore = bleuscore
# print '\t\t\t\t\t\t current bleu: ', bleuscore, ' ; max bleu:', max_bleuscore
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.09,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=3,
maxSentLength=64,
maxDocLength=60,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.00065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus(
rootPath + 'vocab.txt', rootPath + 'mc500.train.tsv_standardlized.txt',
rootPath + 'mc500.test.tsv_standardlized.txt', max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
[
train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
train_Length_D, train_Length_D_s, train_Length_Q, train_Length_A,
train_leftPad_D, train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
train_rightPad_D, train_rightPad_D_s, train_rightPad_Q,
train_rightPad_A
] = train_data
[
test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
test_Length_D, test_Length_D_s, test_Length_Q, test_Length_A,
test_leftPad_D, test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
test_rightPad_D, test_rightPad_D_s, test_rightPad_Q, test_rightPad_A
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A = T.lvector()
y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
len_Q = T.lscalar()
len_A = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_Q = T.lscalar()
left_A = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_Q = T.lscalar()
right_A = T.lscalar()
#wmf=T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_Q_input = embeddings[index_Q.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A_input = embeddings[index_A.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
# load_model_for_conv1([conv_W, conv_b])
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_Q = Conv_with_input_para(
rng,
input=layer0_Q_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A = Conv_with_input_para(
rng,
input=layer0_A_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
layer0_Q_output = debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A_output = debug_print(layer0_A.output, 'layer0_A.output')
layer0_para = [conv_W, conv_b]
layer1_DQ = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_Q_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_Q,
right_r=right_Q,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_Q + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A,
right_r=right_A,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
layer2_DQ = Conv_with_input_para(
rng,
input=layer1_DQ.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA = Conv_with_input_para(
rng,
input=layer1_DA.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#conv single Q and A into doc level with same conv weights
layer2_Q = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DQ.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_Q_output_sent_rep_Dlevel = debug_print(
layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A_output_sent_rep_Dlevel = debug_print(
layer2_A.output_sent_rep_Dlevel, 'layer2_A.output_sent_rep_Dlevel')
layer2_para = [conv2_W, conv2_b]
layer3_DQ = Average_Pooling_for_Top(
rng,
input_l=layer2_DQ.output,
input_r=layer2_Q_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA = Average_Pooling_for_Top(
rng,
input_l=layer2_DA.output,
input_r=layer2_A_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
high_W, high_b = create_highw_para(rng, nkerns[0], nkerns[1])
transform_gate_DQ = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b),
'transform_gate_DQ')
transform_gate_DA = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA.output_D_sent_level_rep) + high_b),
'transform_gate_DA')
transform_gate_Q = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b),
'transform_gate_Q')
transform_gate_A = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA.output_QA_sent_level_rep) + high_b),
'transform_gate_A')
highW_para = [high_W, high_b]
overall_D_Q = debug_print(
(1.0 - transform_gate_DQ) * layer1_DQ.output_D_sent_level_rep +
transform_gate_DQ * layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A = (
1.0 - transform_gate_DA
) * layer1_DA.output_D_sent_level_rep + transform_gate_DA * layer3_DA.output_D_doc_level_rep
overall_Q = (
1.0 - transform_gate_Q
) * layer1_DQ.output_QA_sent_level_rep + transform_gate_Q * layer2_Q.output_sent_rep_Dlevel
overall_A = (
1.0 - transform_gate_A
) * layer1_DA.output_QA_sent_level_rep + transform_gate_A * layer2_A.output_sent_rep_Dlevel
simi_sent_level = debug_print(
cosine(
layer1_DQ.output_D_sent_level_rep +
layer1_DA.output_D_sent_level_rep,
layer1_DQ.output_QA_sent_level_rep +
layer1_DA.output_QA_sent_level_rep), 'simi_sent_level')
simi_doc_level = debug_print(
cosine(
layer3_DQ.output_D_doc_level_rep +
layer3_DA.output_D_doc_level_rep,
layer2_Q.output_sent_rep_Dlevel + layer2_A.output_sent_rep_Dlevel),
'simi_doc_level')
simi_overall_level = debug_print(
cosine(overall_D_Q + overall_D_A, overall_Q + overall_A),
'simi_overall_level')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
layer4_input = debug_print(
T.concatenate([simi_sent_level, simi_doc_level, simi_overall_level],
axis=1),
'layer4_input') #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4 = LogisticRegression(rng, input=layer4_input, n_in=3, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer4.W**2).sum() + (high_W**2).sum() + (conv2_W**2).sum() +
(conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer4.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
#
# [train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
# train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A,
# train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
# train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A]=train_data
# [test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
# test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A,
# test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
# test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A]=test_data
# index = T.lscalar()
# index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
# index_A= T.lvector()
#
# y = T.lvector()
# len_D=T.lscalar()
# len_D_s=T.lvector()
# len_Q=T.lscalar()
# len_A=T.lscalar()
#
# left_D=T.lscalar()
# left_D_s=T.lvector()
# left_Q=T.lscalar()
# left_A=T.lscalar()
#
# right_D=T.lscalar()
# right_D_s=T.lvector()
# right_Q=T.lscalar()
# right_A=T.lscalar()
#
#
# #wmf=T.dmatrix()
# cost_tmp=T.dscalar()
test_model = theano.function(
[index],
[layer4.errors(y), layer4_input, y, layer4.prop_for_posi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A: test_data_A[index],
y: test_Y[index:index + batch_size],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A: test_Length_A[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A: test_leftPad_A[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A: test_rightPad_A[index]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params + layer2_para + layer0_para + highW_para
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index + batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer4.errors(y), layer4_input, y],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index + batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
# readfile=open('/mounts/data/proj/wenpeng/Dataset/SICK/train_plus_dev.txt', 'r')
# train_pairs=[]
# train_y=[]
# for line in readfile:
# tokens=line.strip().split('\t')
# listt=tokens[0]+'\t'+tokens[1]
# train_pairs.append(listt)
# train_y.append(tokens[2])
# readfile.close()
# writefile=open('/mounts/data/proj/wenpeng/Dataset/SICK/weights_fine_tune.txt', 'w')
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
(batch_start * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(cost_average)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses = []
test_y = []
test_features = []
test_prop = []
for i in test_batch_start:
test_loss, layer3_input, y, posi_prop = test_model(i)
test_prop.append(posi_prop[0][0])
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = compute_test_acc(test_y, test_prop)
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(
kernel='linear'
) #OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results = clf.decision_function(test_features)
lr = linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr = lr.decision_function(test_features)
acc_svm = compute_test_acc(test_y, results)
acc_lr = compute_test_acc(test_y, results_lr)
find_better = False
if acc_svm > max_acc:
max_acc = acc_svm
best_epoch = epoch
find_better = True
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
if acc_lr > max_acc:
max_acc = acc_lr
best_epoch = epoch
find_better = True
print '\t\t\tsvm:', acc_svm, 'lr:', acc_lr, 'nn:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
# if find_better==True:
# store_model_to_file(layer2_para, best_epoch)
# print 'Finished storing best conv params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.5, n_epochs=2000, batch_size=500, emb_size=300, hidden_size=300,
L2_weight=0.0001, para_len_limit=700, q_len_limit=40):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
id2word = {y:x for x,y in overall_word2id.iteritems()}
word2vec=load_word2vec()
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
labels = T.imatrix('labels')
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
paragraph_input = embeddings[paragraph.flatten()].reshape((paragraph.shape[0], paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
#
# # BdGRU(rng, str(0), shape, X, mask, is_train = 1, batch_size = 1, p = 0.5)
#
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
Qs_emb = embeddings[questions.flatten()].reshape((questions.shape[0], questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
questions_reps=questions_model.output_sent_rep_maxpooling.reshape((batch_size, 1, hidden_size)) #(batch, 2*out_size)
#questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1)
#attention distributions
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 2, hidden_size+3) # 3 extra features
norm_W_a1=normalize_matrix(W_a1)
norm_W_a2=normalize_matrix(W_a2)
norm_U_a=normalize_matrix(U_a)
LR_b = theano.shared(value=numpy.zeros((2,),
dtype=theano.config.floatX), # @UndefinedVariable
name='LR_b', borrow=True)
attention_paras=[W_a1, W_a2, U_a, LR_b]
transformed_para_reps=T.tanh(T.dot(para_reps.transpose((0, 2,1)), norm_W_a2))
transformed_q_reps=T.tanh(T.dot(questions_reps, norm_W_a1))
#transformed_q_reps=T.repeat(transformed_q_reps, transformed_para_reps.shape[1], axis=1)
add_both=0.5*(transformed_para_reps+transformed_q_reps)
prior_att=T.concatenate([add_both, normalize_matrix(extraF)], axis=2)
#prior_att=T.concatenate([transformed_para_reps, transformed_q_reps], axis=2)
valid_indices=para_mask.flatten().nonzero()[0]
layer3=LogisticRegression(rng, input=prior_att.reshape((batch_size*prior_att.shape[1], hidden_size+3)), n_in=hidden_size+3, n_out=2, W=norm_U_a, b=LR_b)
#error =layer3.negative_log_likelihood(labels.flatten()[valid_indices])
error = -T.mean(T.log(layer3.p_y_given_x)[valid_indices, labels.flatten()[valid_indices]])#[T.arange(y.shape[0]), y])
distributions=layer3.p_y_given_x[:,-1].reshape((batch_size, para_mask.shape[1]))
#distributions=layer3.y_pred.reshape((batch_size, para_mask.shape[1]))
masked_dis=distributions*para_mask
'''
strength = T.tanh(T.dot(prior_att, norm_U_a)) #(batch, #word, 1)
distributions=debug_print(strength.reshape((batch_size, paragraph.shape[1])), 'distributions')
para_mask=para_mask
masked_dis=distributions*para_mask
# masked_label=debug_print(labels*para_mask, 'masked_label')
# error=((masked_dis-masked_label)**2).mean()
label_mask=T.gt(labels,0.0)
neg_label_mask=T.lt(labels,0.0)
dis_masked=distributions*label_mask
remain_dis_masked=distributions*neg_label_mask
ans_size=T.sum(label_mask)
non_ans_size=T.sum(neg_label_mask)
pos_error=T.sum((dis_masked-label_mask)**2)/ans_size
neg_error=T.sum((remain_dis_masked-(-neg_label_mask))**2)/non_ans_size
error=pos_error+0.5*neg_error #(ans_size*1.0/non_ans_size)*
'''
# def AttentionLayer(q_rep, ext_M):
# theano_U_a=debug_print(norm_U_a, 'norm_U_a')
# prior_att=debug_print(T.nnet.sigmoid(T.dot(q_rep, norm_W_a1).reshape((1, hidden_size)) + T.dot(paragraph_model.output_matrix.transpose(), norm_W_a2)), 'prior_att')
# f __name__ == '__main__':
# prior_att=T.concatenate([prior_att, ext_M], axis=1)
#
# strength = debug_print(T.tanh(T.dot(prior_att, theano_U_a)), 'strength') #(#word, 1)
# return strength.transpose() #(1, #words)
# distributions, updates = theano.scan(
# AttentionLayer,
# sequences=[questions_reps,extraF] )
# distributions=debug_print(distributions.reshape((questions.shape[0],paragraph.shape[0])), 'distributions')
# labels=debug_print(labels, 'labels')
# label_mask=T.gt(labels,0.0)
# neg_label_mask=T.lt(labels,0.0)
# dis_masked=distributions*label_mask
# remain_dis_masked=distributions*neg_label_mask
# pos_error=((dis_masked-1)**2).mean()
# neg_error=((remain_dis_masked-(-1))**2).mean()
# error=pos_error+(T.sum(label_mask)*1.0/T.sum(neg_label_mask))*neg_error
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [embeddings]+paragraph_para+Q_para+attention_paras
L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ, UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=error#+L2_weight*L2_reg
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([paragraph, questions,labels, para_mask, q_mask, extraF], error, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF], masked_dis, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_exact_acc=0.0
cost_i=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#shuffle(train_batch_start)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
np.asarray(train_para_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_Q_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_label_list[para_id:para_id+batch_size], dtype='int32'),
np.asarray(train_para_mask[para_id:para_id+batch_size], dtype=theano.config.floatX),
np.asarray(train_mask[para_id:para_id+batch_size], dtype=theano.config.floatX),
np.asarray(train_feature_matrixlist[para_id:para_id+batch_size], dtype=theano.config.floatX))
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
q_amount=0
for test_para_id in test_batch_start:
distribution_matrix=test_model(
np.asarray(test_para_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_Q_list[test_para_id:test_para_id+batch_size], dtype='int32'),
np.asarray(test_para_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_mask[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX),
np.asarray(test_feature_matrixlist[test_para_id:test_para_id+batch_size], dtype=theano.config.floatX))
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+batch_size]
para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+batch_size]
paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
para_len=len(test_para_wordlist_list[0])
if para_len!=len(distribution_matrix[0]):
print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
exit(0)
# q_size=len(distribution_matrix)
q_amount+=batch_size
# print q_size
# print test_para_word_list
for q in range(batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], np.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q])
q_gold_ans_set=para_gold_ansset_list[q]
F1=MacroF1(pred_ans, q_gold_ans_set)
exact_match+=F1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
exact_acc=exact_match/q_amount
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
print 'current average F1:', exact_acc, '\t\tmax F1:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.085,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=7,
maxSentLength=60,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.00005,
update_freq=10,
norm_threshold=5.0):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_msr_corpus(rootPath + 'vocab.txt',
rootPath + 'tokenized_train.txt',
rootPath + 'tokenized_test.txt',
maxSentLength)
mtPath = '/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
mt_train, mt_test = load_mts(mtPath + 'concate_15mt_train.txt',
mtPath + 'concate_15mt_test.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int32')
indices_train_r = T.cast(indices_train_r, 'int32')
indices_test_l = T.cast(indices_test_l, 'int32')
indices_test_r = T.cast(indices_test_r, 'int32')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
cost_tmp = 0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.imatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.imatrix('x_index_r')
y = T.ivector('y')
left_l = T.iscalar()
right_l = T.iscalar()
left_r = T.iscalar()
right_r = T.iscalar()
length_l = T.iscalar()
length_r = T.iscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_size[0],
filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng,
input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_r = Conv_with_input_para(rng,
input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output')
layer0_para = [conv_W, conv_b]
layer1 = Average_Pooling(rng,
input_l=layer0_l_output,
input_r=layer0_r_output,
kern=nkerns[0],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1,
window_size=window_width,
maxSentLength=maxSentLength)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]))
layer2_l = Conv_with_input_para(rng,
input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0],
ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]),
W=conv2_W,
b=conv2_b)
layer2_r = Conv_with_input_para(rng,
input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0],
ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]),
W=conv2_W,
b=conv2_b)
layer2_para = [conv2_W, conv2_b]
layer3 = Average_Pooling_for_batch1(rng,
input_l=layer2_l.output,
input_r=layer2_r.output,
kern=nkerns[1],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1)
layer3_out = debug_print(layer3.output_simi, 'layer1_out')
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
#norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
#norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
'''
uni_cosine=cosine(sum_uni_l, sum_uni_r)
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer4_input = T.concatenate(
[mts, eucli_1, layer1.output_eucli, layer3_out, len_l, len_r],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4 = LogisticRegression(rng,
input=layer4_input,
n_in=15 + 3 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer4.W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()
cost_this = debug_print(layer4.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
test_model = theano.function(
[index], [layer4.errors(y), layer4.y_pred],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params + layer2_para + layer0_para # + layer1.params
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
#norm=T.sqrt((grad_i**2).sum())
#if T.lt(norm_threshold, norm):
# print 'big norm'
# grad_i=grad_i*(norm_threshold/norm)
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index], [cost, layer4.errors(y), layer4_input],
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer4.errors(y)],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
if iter % update_freq != 0:
cost_ij, error_ij = train_model_predict(batch_start)
#print 'cost_ij: ', cost_ij
cost_tmp += cost_ij
error_sum += error_ij
else:
cost_average, error_ij, layer3_input = train_model(batch_start)
#print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' sum error: '+str(error_sum)+'/'+str(update_freq)
error_sum = 0
cost_tmp = 0 #reset for the next batch
#print layer3_input
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses = []
for i in test_batch_start:
test_loss, pred_y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
print((
'\t\t\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
#print 'validating & testing...'
# compute zero-one loss on validation set
validation_losses = []
for i in dev_batch_start:
time.sleep(0.5)
validation_losses.append(validate_model(i))
#validation_losses = [validate_model(i) for i in dev_batch_start]
this_validation_loss = numpy.mean(validation_losses)
print('\t\tepoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index , n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in test_batch_start]
test_score = numpy.mean(test_losses)
print(('\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, word_nkerns=50, char_nkerns=4, batch_size=1, window_width=[2, 5],
emb_size=50, char_emb_size=4, hidden_size=200,
margin=0.5, L2_weight=0.0003, Div_reg=0.03, update_freq=1, norm_threshold=5.0, max_truncate=40,
max_char_len=40, max_des_len=20, max_relation_len=5, max_Q_len=30, train_neg_size=21,
neg_all=100, train_size=200, test_size=200, mark='_forfun'): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/freebase/SimpleQuestions_v2/'
triple_files=['annotated_fb_data_train.entitylinking.top20_succSet_asInput.txt', 'annotated_fb_data_test.entitylinking.top20_succSet_asInput.txt']
rng = numpy.random.RandomState(23455)
datasets, datasets_test, length_per_example_test, vocab_size, char_size=load_train(triple_files[0], triple_files[1], max_char_len, max_des_len, max_relation_len, max_Q_len, train_size, test_size, mark)#max_char_len, max_des_len, max_relation_len, max_Q_len
print 'vocab_size:', vocab_size, 'char_size:', char_size
train_data=datasets
# valid_data=datasets[1]
test_data=datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
train_pos_entity_char=train_data[0]
train_pos_entity_des=train_data[1]
train_relations=train_data[2]
train_entity_char_lengths=train_data[3]
train_entity_des_lengths=train_data[4]
train_relation_lengths=train_data[5]
train_mention_char_ids=train_data[6]
train_remainQ_word_ids=train_data[7]
train_mention_char_lens=train_data[8]
train_remainQ_word_len=train_data[9]
train_entity_scores=train_data[10]
test_pos_entity_char=test_data[0]
test_pos_entity_des=test_data[1]
test_relations=test_data[2]
test_entity_char_lengths=test_data[3]
test_entity_des_lengths=test_data[4]
test_relation_lengths=test_data[5]
test_mention_char_ids=test_data[6]
test_remainQ_word_ids=test_data[7]
test_mention_char_lens=test_data[8]
test_remainQ_word_len=test_data[9]
test_entity_scores=test_data[10]
#
# test_pos_entity_char=test_data[0] #matrix, each row for line example, all head and tail entity, iteratively: 40*2*51
# test_pos_entity_des=test_data[1] #matrix, each row for a examle: 20*2*51
# test_relations=test_data[2] #matrix, each row for a example: 5*51
# test_entity_char_lengths=test_data[3] #matrix, each row for a example: 3*2*51 (three valies for one entity)
# test_entity_des_lengths=test_data[4] #matrix, each row for a example: 3*2*51 (three values for one entity)
# test_relation_lengths=test_data[5] #matrix, each row for a example: 3*51
# test_mention_char_ids=test_data[6] #matrix, each row for a mention: 40
# test_remainQ_word_ids=test_data[7] #matrix, each row for a question: 30
# test_mention_char_lens=test_data[8] #matrix, each three values for a mention: 3
# test_remainQ_word_len=test_data[9] #matrix, each three values for a remain question: 3
train_sizes=[len(train_pos_entity_char), len(train_pos_entity_des), len(train_relations), len(train_entity_char_lengths), len(train_entity_des_lengths),\
len(train_relation_lengths), len(train_mention_char_ids), len(train_remainQ_word_ids), len(train_mention_char_lens), len(train_remainQ_word_len), len(train_entity_scores)]
if sum(train_sizes)/len(train_sizes)!=train_size:
print 'weird size:', train_sizes
exit(0)
test_sizes=[len(test_pos_entity_char), len(test_pos_entity_des), len(test_relations), len(test_entity_char_lengths), len(test_entity_des_lengths),\
len(test_relation_lengths), len(test_mention_char_ids), len(test_remainQ_word_ids), len(test_mention_char_lens), len(test_remainQ_word_len), len(test_entity_scores)]
if sum(test_sizes)/len(test_sizes)!=test_size:
print 'weird size:', test_sizes
exit(0)
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_pos_entity_char=pythonList_into_theanoIntMatrix(train_pos_entity_char)
indices_train_pos_entity_des=pythonList_into_theanoIntMatrix(train_pos_entity_des)
indices_train_relations=pythonList_into_theanoIntMatrix(train_relations)
indices_train_entity_char_lengths=pythonList_into_theanoIntMatrix(train_entity_char_lengths)
indices_train_entity_des_lengths=pythonList_into_theanoIntMatrix(train_entity_des_lengths)
indices_train_relation_lengths=pythonList_into_theanoIntMatrix(train_relation_lengths)
indices_train_mention_char_ids=pythonList_into_theanoIntMatrix(train_mention_char_ids)
indices_train_remainQ_word_ids=pythonList_into_theanoIntMatrix(train_remainQ_word_ids)
indices_train_mention_char_lens=pythonList_into_theanoIntMatrix(train_mention_char_lens)
indices_train_remainQ_word_len=pythonList_into_theanoIntMatrix(train_remainQ_word_len)
indices_train_entity_scores=pythonList_into_theanoFloatMatrix(train_entity_scores)
# indices_test_pos_entity_char=pythonList_into_theanoIntMatrix(test_pos_entity_char)
# indices_test_pos_entity_des=pythonList_into_theanoIntMatrix(test_pos_entity_des)
# indices_test_relations=pythonList_into_theanoIntMatrix(test_relations)
# indices_test_entity_char_lengths=pythonList_into_theanoIntMatrix(test_entity_char_lengths)
# indices_test_entity_des_lengths=pythonList_into_theanoIntMatrix(test_entity_des_lengths)
# indices_test_relation_lengths=pythonList_into_theanoIntMatrix(test_relation_lengths)
# indices_test_mention_char_ids=pythonList_into_theanoIntMatrix(test_mention_char_ids)
# indices_test_remainQ_word_ids=pythonList_into_theanoIntMatrix(test_remainQ_word_ids)
# indices_test_mention_char_lens=pythonList_into_theanoIntMatrix(test_mention_char_lens)
# indices_test_remainQ_word_len=pythonList_into_theanoIntMatrix(test_remainQ_word_len)
# indices_test_entity_scores=pythonList_into_theanoIntMatrix(test_entity_scores)
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'word_emb'+mark+'.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, numpy.random.RandomState(1234))
char_rand_values[0]=numpy.array(numpy.zeros(char_emb_size),dtype=theano.config.floatX)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.lscalar()
chosed_indices=T.lvector()
ent_char_ids_M = T.lmatrix()
ent_lens_M = T.lmatrix()
men_char_ids_M = T.lmatrix()
men_lens_M=T.lmatrix()
rel_word_ids_M=T.lmatrix()
rel_word_lens_M=T.lmatrix()
desH_word_ids_M=T.lmatrix()
desH_word_lens_M=T.lmatrix()
# desT_word_ids_M=T.lmatrix()
# desT_word_lens_M=T.lmatrix()
q_word_ids_M=T.lmatrix()
q_word_lens_M=T.lmatrix()
ent_scores=T.dvector()
#max_char_len, max_des_len, max_relation_len, max_Q_len
# ent_men_ishape = (char_emb_size, max_char_len) # this is the size of MNIST images
# rel_ishape=(emb_size, max_relation_len)
# des_ishape=(emb_size, max_des_len)
# q_ishape=(emb_size, max_Q_len)
filter_size=(emb_size,window_width[0])
char_filter_size=(char_emb_size, window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
char_filter_shape=(char_nkerns, 1, char_filter_size[0], char_filter_size[1])
word_filter_shape=(word_nkerns, 1, filter_size[0], filter_size[1])
char_conv_W, char_conv_b=create_conv_para(rng, filter_shape=char_filter_shape)
q_rel_conv_W, q_rel_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
q_desH_conv_W, q_desH_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
params = [char_embeddings, embeddings, char_conv_W, char_conv_b, q_rel_conv_W, q_rel_conv_b, q_desH_conv_W, q_desH_conv_b]
char_conv_W_into_matrix=char_conv_W.reshape((char_conv_W.shape[0], char_conv_W.shape[2]*char_conv_W.shape[3]))
q_rel_conv_W_into_matrix=q_rel_conv_W.reshape((q_rel_conv_W.shape[0], q_rel_conv_W.shape[2]*q_rel_conv_W.shape[3]))
q_desH_conv_W_into_matrix=q_desH_conv_W.reshape((q_desH_conv_W.shape[0], q_desH_conv_W.shape[2]*q_desH_conv_W.shape[3]))
# load_model_from_file(rootPath, params, '')
def SimpleQ_matches_Triple(ent_char_ids_f,ent_lens_f,rel_word_ids_f,rel_word_lens_f,desH_word_ids_f,
desH_word_lens_f,
men_char_ids_f, q_word_ids_f, men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng, input=ent_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
men_char_conv = Conv_with_input_para(rng, input=men_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
#q_desH
q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
desH_conv = Conv_with_input_para(rng, input=desH_word_input,
image_shape=(batch_size, 1, emb_size, max_des_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
# #q_desT
# q_desT_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# desT_conv = Conv_with_input_para(rng, input=desT_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# ent_char_output=debug_print(ent_char_conv.output, 'ent_char.output')
# men_char_output=debug_print(men_char_conv.output, 'men_char.output')
ent_conv_pool=Max_Pooling(rng, input_l=ent_char_conv.output, left_l=ent_lens_f[0], right_l=ent_lens_f[2])
men_conv_pool=Max_Pooling(rng, input_l=men_char_conv.output, left_l=men_lens_f[0], right_l=men_lens_f[2])
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
# q_desT_pool=Max_Pooling(rng, input_l=q_desT_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
# desT_conv_pool=Max_Pooling(rng, input_l=desT_conv.output, left_l=desT_word_lens_f[0], right_l=desT_word_lens_f[2])
overall_simi=(cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)+\
cosine(q_rel_pool.topk_max_pooling, rel_conv_pool.output_maxpooling)+\
0.1*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling))/3.0
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(
SimpleQ_matches_Triple,
sequences=[ent_char_ids_M,ent_lens_M,rel_word_ids_M,rel_word_lens_M,desH_word_ids_M,
desH_word_lens_M,
men_char_ids_M, q_word_ids_M, men_lens_M, q_word_lens_M])
simi_list+=0.5*ent_scores
posi_simi=simi_list[0]
nega_simies=simi_list[1:]
loss_simi_list=T.maximum(0.0, margin-posi_simi.reshape((1,1))+nega_simies)
loss_simi=T.mean(loss_simi_list)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((char_embeddings** 2).sum()+(embeddings** 2).sum()+(char_conv_W** 2).sum()+(q_rel_conv_W** 2).sum()+(q_desH_conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg= Diversify_Reg(char_conv_W_into_matrix)+Diversify_Reg(q_rel_conv_W_into_matrix)+Diversify_Reg(q_desH_conv_W_into_matrix)
cost=loss_simi+L2_weight*L2_reg+Div_reg*diversify_reg
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([ent_char_ids_M, ent_lens_M, men_char_ids_M, men_lens_M, rel_word_ids_M, rel_word_lens_M, desH_word_ids_M, desH_word_lens_M,
q_word_ids_M, q_word_lens_M, ent_scores], [loss_simi, simi_list],on_unused_input='ignore')
# givens={
# ent_char_ids_M : test_pos_entity_char[index].reshape((length_per_example_test[index], max_char_len)),
# ent_lens_M : test_entity_char_lengths[index].reshape((length_per_example_test[index], 3)),
# men_char_ids_M : test_mention_char_ids[index].reshape((length_per_example_test[index], max_char_len)),
# men_lens_M : test_mention_char_lens[index].reshape((length_per_example_test[index], 3)),
# rel_word_ids_M : test_relations[index].reshape((length_per_example_test[index], max_relation_len)),
# rel_word_lens_M : test_relation_lengths[index].reshape((length_per_example_test[index], 3)),
# desH_word_ids_M : test_pos_entity_des[index].reshape((length_per_example_test[index], max_des_len)),
# desH_word_lens_M : test_entity_des_lengths[index].reshape((length_per_example_test[index], 3)),
# # desT_word_ids_M : indices_train_pos_entity_des[index].reshape(((neg_all)*2, max_des_len))[1::2],
# # desT_word_lens_M : indices_train_entity_des_lengths[index].reshape(((neg_all)*2, 3))[1::2],
# q_word_ids_M : test_remainQ_word_ids[index].reshape((length_per_example_test[index], max_Q_len)),
# q_word_lens_M : test_remainQ_word_len[index].reshape((length_per_example_test[index], 3)),
# ent_scores : test_entity_scores[index]},
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
#+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
# updates.append((acc_i, acc))
if param_i == embeddings:
updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))[0], theano.shared(numpy.zeros(emb_size))))) #Ada
elif param_i == char_embeddings:
updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))[0], theano.shared(numpy.zeros(char_emb_size))))) #AdaGrad
else:
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index, chosed_indices], [loss_simi, cost], updates=updates,
givens={
ent_char_ids_M : indices_train_pos_entity_char[index].reshape((neg_all, max_char_len))[chosed_indices].reshape((train_neg_size, max_char_len)),
ent_lens_M : indices_train_entity_char_lengths[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
men_char_ids_M : indices_train_mention_char_ids[index].reshape((neg_all, max_char_len))[chosed_indices].reshape((train_neg_size, max_char_len)),
men_lens_M : indices_train_mention_char_lens[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
rel_word_ids_M : indices_train_relations[index].reshape((neg_all, max_relation_len))[chosed_indices].reshape((train_neg_size, max_relation_len)),
rel_word_lens_M : indices_train_relation_lengths[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
desH_word_ids_M : indices_train_pos_entity_des[index].reshape((neg_all, max_des_len))[chosed_indices].reshape((train_neg_size, max_des_len)),
desH_word_lens_M : indices_train_entity_des_lengths[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
# desT_word_ids_M : indices_train_pos_entity_des[index].reshape(((neg_all)*2, max_des_len))[1::2],
# desT_word_lens_M : indices_train_entity_des_lengths[index].reshape(((neg_all)*2, 3))[1::2],
q_word_ids_M : indices_train_remainQ_word_ids[index].reshape((neg_all, max_Q_len))[chosed_indices].reshape((train_neg_size, max_Q_len)),
q_word_lens_M : indices_train_remainQ_word_len[index].reshape((neg_all, 3))[chosed_indices].reshape((train_neg_size, 3)),
ent_scores : indices_train_entity_scores[index][chosed_indices]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
best_test_accu=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#print batch_start
sample_indices=[0]+random.sample(range(1, neg_all), train_neg_size-1)
loss_simi_i, cost_i= train_model(batch_start, sample_indices)
# if batch_start%1==0:
# print batch_start, '\t loss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
# store_model_to_file(rootPath, params)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+'\tloss_simi_i: ', loss_simi_i, 'cost_i:', cost_i
#if iter ==1:
# exit(0)
#
if iter % n_train_batches == 0:
test_loss=[]
succ=0
for i in range(test_size):
# print 'testing', i, '...'
#prepare data
test_ent_char_ids_M= numpy.asarray(test_pos_entity_char[i], dtype='int64').reshape((length_per_example_test[i], max_char_len))
test_ent_lens_M = numpy.asarray(test_entity_char_lengths[i], dtype='int64').reshape((length_per_example_test[i], 3))
test_men_char_ids_M = numpy.asarray(test_mention_char_ids[i], dtype='int64').reshape((length_per_example_test[i], max_char_len))
test_men_lens_M = numpy.asarray(test_mention_char_lens[i], dtype='int64').reshape((length_per_example_test[i], 3))
test_rel_word_ids_M = numpy.asarray(test_relations[i], dtype='int64').reshape((length_per_example_test[i], max_relation_len))
test_rel_word_lens_M = numpy.asarray(test_relation_lengths[i], dtype='int64').reshape((length_per_example_test[i], 3))
test_desH_word_ids_M =numpy.asarray( test_pos_entity_des[i], dtype='int64').reshape((length_per_example_test[i], max_des_len))
test_desH_word_lens_M = numpy.asarray(test_entity_des_lengths[i], dtype='int64').reshape((length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(test_remainQ_word_ids[i], dtype='int64').reshape((length_per_example_test[i], max_Q_len))
test_q_word_lens_M = numpy.asarray(test_remainQ_word_len[i], dtype='int64').reshape((length_per_example_test[i], 3))
test_ent_scores = numpy.asarray(test_entity_scores[i], dtype=theano.config.floatX)
loss_simi_i,simi_list_i=test_model(test_ent_char_ids_M, test_ent_lens_M, test_men_char_ids_M, test_men_lens_M, test_rel_word_ids_M, test_rel_word_lens_M,
test_desH_word_ids_M, test_desH_word_lens_M, test_q_word_ids_M, test_q_word_lens_M, test_ent_scores)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if simi_list_i[0]>=max(simi_list_i[1:]):
succ+=1
# print 'testing', i, '...acc:', succ*1.0/(i+1)
succ=succ*1.0/test_size
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test accu of best '
'model %f') %
(epoch, minibatch_index, n_train_batches,succ))
if best_test_accu< succ:
best_test_accu=succ
store_model_to_file(rootPath, params, mark)
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.001, n_epochs=2000, nkerns=[90,90], batch_size=1, window_width=2,
maxSentLength=64, maxDocLength=60, emb_size=50, hidden_size=200,
L2_weight=0.0065, update_freq=1, norm_threshold=5.0, max_s_length=57, max_d_length=59, margin=0.2):
maxSentLength=max_s_length+2*(window_width-1)
maxDocLength=max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data,train_size, test_data, test_size, vocab_size=load_MCTest_corpus_DPNQ(rootPath+'vocab_DPNQ.txt', rootPath+'mc500.train.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt', rootPath+'mc500.test.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt', max_s_length,maxSentLength, maxDocLength)#vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[train_data_D, train_data_A1, train_data_A2, train_data_A3, train_Label,
train_Length_D,train_Length_D_s, train_Length_A1, train_Length_A2, train_Length_A3,
train_leftPad_D,train_leftPad_D_s, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3,
train_rightPad_D,train_rightPad_D_s, train_rightPad_A1, train_rightPad_A2, train_rightPad_A3]=train_data
[test_data_D, test_data_A1, test_data_A2, test_data_A3, test_Label,
test_Length_D,test_Length_D_s, test_Length_A1, test_Length_A2, test_Length_A3,
test_leftPad_D,test_leftPad_D_s, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3,
test_rightPad_D,test_rightPad_D_s, test_rightPad_A1, test_rightPad_A2, test_rightPad_A3]=test_data
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_DPNQ_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1= T.lvector()
index_A2= T.lvector()
index_A3= T.lvector()
# index_A4= T.lvector()
# y = T.lvector()
len_D=T.lscalar()
len_D_s=T.lvector()
# len_Q=T.lscalar()
len_A1=T.lscalar()
len_A2=T.lscalar()
len_A3=T.lscalar()
# len_A4=T.lscalar()
left_D=T.lscalar()
left_D_s=T.lvector()
# left_Q=T.lscalar()
left_A1=T.lscalar()
left_A2=T.lscalar()
left_A3=T.lscalar()
# left_A4=T.lscalar()
right_D=T.lscalar()
right_D_s=T.lvector()
# right_Q=T.lscalar()
right_A1=T.lscalar()
right_A2=T.lscalar()
right_A3=T.lscalar()
# right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words=(emb_size,window_width)
filter_sents=(nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_Q_input = embeddings[index_Q.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A4_input = embeddings[index_A4.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]))
layer0_para=[conv_W, conv_b]
conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
layer2_para=[conv2_W, conv2_b]
high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1]) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para=[high_W, high_b]
params = layer2_para+layer0_para+highW_para#+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(rng, input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(rng, input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A3 = Conv_with_input_para(rng, input=layer0_A3_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A4 = Conv_with_input_para(rng, input=layer0_A4_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_D_output=debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output=debug_print(layer0_A1.output, 'layer0_A1.output')
layer0_A2_output=debug_print(layer0_A2.output, 'layer0_A2.output')
layer0_A3_output=debug_print(layer0_A3.output, 'layer0_A3.output')
# layer0_A4_output=debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A1_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A1, right_r=right_A1,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A1+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA2=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A2_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A2, right_r=right_A2,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A2+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA3=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A3_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A3, right_r=right_A3,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A3+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA4=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A4_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A4, right_r=right_A4,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A4+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(rng, input=layer1_DA1.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA2 = Conv_with_input_para(rng, input=layer1_DA2.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA3 = Conv_with_input_para(rng, input=layer1_DA3.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA4 = Conv_with_input_para(rng, input=layer1_DA4.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA1.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A2 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA2.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A3 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA3.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A4 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA4.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel=debug_print(layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer2_A2_output_sent_rep_Dlevel=debug_print(layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
layer2_A3_output_sent_rep_Dlevel=debug_print(layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
# layer2_A4_output_sent_rep_Dlevel=debug_print(layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1=Average_Pooling_for_Top(rng, input_l=layer2_DA1.output, input_r=layer2_A1_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA2=Average_Pooling_for_Top(rng, input_l=layer2_DA2.output, input_r=layer2_A2_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA3=Average_Pooling_for_Top(rng, input_l=layer2_DA3.output, input_r=layer2_A3_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA4=Average_Pooling_for_Top(rng, input_l=layer2_DA4.output, input_r=layer2_A4_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b), 'transform_gate_DA1')
transform_gate_DA2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_D_sent_level_rep) + high_b), 'transform_gate_DA2')
transform_gate_DA3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_D_sent_level_rep) + high_b), 'transform_gate_DA3')
# transform_gate_DA4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_D_sent_level_rep) + high_b), 'transform_gate_DA4')
# transform_gate_Q=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b), 'transform_gate_Q')
transform_gate_A1=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b), 'transform_gate_A1')
transform_gate_A2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b), 'transform_gate_A2')
# transform_gate_A3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b), 'transform_gate_A3')
# transform_gate_A4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b), 'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1=(1.0-transform_gate_DA1)*layer1_DA1.output_D_sent_level_rep+transform_gate_DA1*layer3_DA1.output_D_doc_level_rep
overall_D_A2=(1.0-transform_gate_DA2)*layer1_DA2.output_D_sent_level_rep+transform_gate_DA2*layer3_DA2.output_D_doc_level_rep
overall_D_A3=(1.0-transform_gate_DA3)*layer1_DA3.output_D_sent_level_rep+transform_gate_DA3*layer3_DA3.output_D_doc_level_rep
# overall_D_A4=(1.0-transform_gate_DA4)*layer1_DA4.output_D_sent_level_rep+transform_gate_DA4*layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1=(1.0-transform_gate_A1)*layer1_DA1.output_QA_sent_level_rep+transform_gate_A1*layer2_A1.output_sent_rep_Dlevel
overall_A2=(1.0-transform_gate_A2)*layer1_DA2.output_QA_sent_level_rep+transform_gate_A2*layer2_A2.output_sent_rep_Dlevel
# overall_A3=(1.0-transform_gate_A3)*layer1_DA3.output_QA_sent_level_rep+transform_gate_A3*layer2_A3.output_sent_rep_Dlevel
# overall_A4=(1.0-transform_gate_A4)*layer1_DA4.output_QA_sent_level_rep+transform_gate_A4*layer2_A4.output_sent_rep_Dlevel
simi_sent_level1=debug_print(cosine(layer1_DA1.output_D_sent_level_rep, layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_sent_level2=debug_print(cosine(layer1_DA2.output_D_sent_level_rep, layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
# simi_sent_level3=debug_print(cosine(layer1_DA3.output_D_sent_level_rep, layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
# simi_sent_level4=debug_print(cosine(layer1_DA4.output_D_sent_level_rep, layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1=debug_print(cosine(layer3_DA1.output_D_doc_level_rep, layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_doc_level2=debug_print(cosine(layer3_DA2.output_D_doc_level_rep, layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
# simi_doc_level3=debug_print(cosine(layer3_DA3.output_D_doc_level_rep, layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
# simi_doc_level4=debug_print(cosine(layer3_DA4.output_D_doc_level_rep, layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1=debug_print(cosine(overall_D_A1, overall_A1), 'simi_overall_level1')
simi_overall_level2=debug_print(cosine(overall_D_A2, overall_A2), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(overall_D_A3, overall_A3), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(overall_D_A4, overall_A4), 'simi_overall_level4')
# simi_1=simi_overall_level1+simi_sent_level1+simi_doc_level1
# simi_2=simi_overall_level2+simi_sent_level2+simi_doc_level2
simi_1=(simi_overall_level1+simi_sent_level1+simi_doc_level1)/3.0
simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)
simi_PQ=cosine(layer1_DA1.output_QA_sent_level_rep, layer1_DA3.output_D_sent_level_rep)
simi_NQ=cosine(layer1_DA2.output_QA_sent_level_rep, layer1_DA3.output_D_sent_level_rep)
#bad matching at overall level
# simi_PQ=cosine(overall_A1, overall_D_A3)
# simi_NQ=cosine(overall_A2, overall_D_A3)
match_cost=T.maximum(0.0, margin+simi_NQ-simi_PQ)
cost=T.maximum(0.0, margin+simi_sent_level2-simi_sent_level1)+T.maximum(0.0, margin+simi_doc_level2-simi_doc_level1)+T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)
cost=cost#+match_cost
# posi_simi=simi_1
# nega_simi=simi_2
L2_reg =debug_print((high_W**2).sum()+3*(conv2_W**2).sum()+(conv_W**2).sum(), 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
# index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
# len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
# left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index]
# right_A4: test_rightPad_A4[index]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2], updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index]
# right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2],
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index]
# right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
posi_train_sent=[]
nega_train_sent=[]
posi_train_doc=[]
nega_train_doc=[]
posi_train_overall=[]
nega_train_overall=[]
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
sys.stdout.write( "Training :[%6f] %% complete!\r" % ((iter%train_size)*100.0/train_size) )
sys.stdout.flush()
minibatch_index=minibatch_index+1
cost_average, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2= train_model(batch_start)
posi_train_sent.append(simi_sent_level1)
nega_train_sent.append(simi_sent_level2)
posi_train_doc.append(simi_doc_level1)
nega_train_doc.append(simi_doc_level2)
posi_train_overall.append(simi_overall_level1)
nega_train_overall.append(simi_overall_level2)
if iter % n_train_batches == 0:
corr_train_sent=compute_corr(posi_train_sent, nega_train_sent)
corr_train_doc=compute_corr(posi_train_doc, nega_train_doc)
corr_train_overall=compute_corr(posi_train_overall, nega_train_overall)
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+'corr rate:'+str(corr_train_sent*300.0/train_size)+' '+str(corr_train_doc*300.0/train_size)+' '+str(corr_train_overall*300.0/train_size)
if iter % validation_frequency == 0:
posi_test_sent=[]
nega_test_sent=[]
posi_test_doc=[]
nega_test_doc=[]
posi_test_overall=[]
nega_test_overall=[]
for i in test_batch_start:
cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2=test_model(i)
posi_test_sent.append(simi_sent_level1)
nega_test_sent.append(simi_sent_level2)
posi_test_doc.append(simi_doc_level1)
nega_test_doc.append(simi_doc_level2)
posi_test_overall.append(simi_overall_level1)
nega_test_overall.append(simi_overall_level2)
corr_test_sent=compute_corr(posi_test_sent, nega_test_sent)
corr_test_doc=compute_corr(posi_test_doc, nega_test_doc)
corr_test_overall=compute_corr(posi_test_overall, nega_test_overall)
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc_sent=corr_test_sent*1.0/(test_size/3.0)
test_acc_doc=corr_test_doc*1.0/(test_size/3.0)
test_acc_overall=corr_test_overall*1.0/(test_size/3.0)
#test_acc=1-test_score
# print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
# 'model %f %%') %
# (epoch, minibatch_index, n_train_batches,test_acc * 100.))
print '\t\t\tepoch', epoch, ', minibatch', minibatch_index, '/', n_train_batches, 'test acc of best model', test_acc_sent*100,test_acc_doc*100,test_acc_overall*100
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better=False
if test_acc_sent > max_acc:
max_acc=test_acc_sent
best_epoch=epoch
find_better=True
if test_acc_doc > max_acc:
max_acc=test_acc_doc
best_epoch=epoch
find_better=True
if test_acc_overall > max_acc:
max_acc=test_acc_overall
best_epoch=epoch
find_better=True
print '\t\t\tmax:', max_acc,'(at',best_epoch,')'
if find_better==True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(file_name,
vocab_file,
train_file,
dev_file,
word2vec_file,
learning_rate=0.001,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=128,
max_d_length=128,
margin=0.3):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
f = open(file_name, 'w')
f.write("model options " + str(model_options) + '\n')
rng = numpy.random.RandomState(23455)
train_data, _train_Label, train_size, test_data, _test_Label, test_size, vocab_size = load_MCTest_corpus_DPN(
vocab_file, train_file, dev_file, max_s_length, maxSentLength,
maxDocLength) #vocab_size contain train, dev and test
f.write('train_size : ' + str(train_size))
[
train_data_D, train_data_A1, train_Label, train_Length_D,
train_Length_D_s, train_Length_A1, train_leftPad_D, train_leftPad_D_s,
train_leftPad_A1, train_rightPad_D, train_rightPad_D_s,
train_rightPad_A1
] = train_data
[
test_data_D, test_data_A1, test_Label, test_Length_D, test_Length_D_s,
test_Length_A1, test_leftPad_D, test_leftPad_D_s, test_leftPad_A1,
test_rightPad_D, test_rightPad_D_s, test_rightPad_A1
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
rand_values = load_word2vec_to_init(rand_values, word2vec_file)
embeddings = theano.shared(value=rand_values, borrow=True)
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_A1 = T.lvector()
y = T.lscalar()
len_D = T.lscalar()
len_D_s = T.lvector()
len_A1 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_A1 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_A1 = T.lscalar()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
######################
# BUILD ACTUAL MODEL #
######################
f.write('... building the model\n')
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(
rng, nkerns[0], nkerns[1]
) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
simi_1 = (simi_overall_level1 + simi_sent_level1 + simi_doc_level1) / 3.0
logistic_w, logistic_b = create_logistic_para(rng, 1, 2)
logistic_para = [logistic_w, logistic_b]
params += logistic_para
simi_1 = T.dot(logistic_w, simi_1) + logistic_b.dimshuffle(0, 'x')
simi_1 = simi_1.dimshuffle(1, 0)
simi_1 = T.nnet.softmax(simi_1)
predict = T.argmax(simi_1, axis=1)
tmp = T.log(simi_1)
cost = T.maximum(0.0, margin + tmp[0][1 - y] - tmp[0][y])
L2_reg = (high_W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum() + (
logistic_w**2).sum()
cost = cost + L2_weight * L2_reg
test_model = theano.function(
[index],
[cost, simi_1, predict],
givens={
index_D: test_data_D[index], #a matrix
index_A1: test_data_A1[index],
y: test_Label[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_A1: test_Length_A1[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_A1: test_leftPad_A1[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_A1: test_rightPad_A1[index],
},
on_unused_input='ignore')
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index], [cost, simi_1, predict],
updates=updates,
givens={
index_D: train_data_D[index],
index_A1: train_data_A1[index],
y: train_Label[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_A1: train_Length_A1[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_A1: train_leftPad_A1[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_A1: train_rightPad_A1[index],
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
f.write('... training\n')
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
simi_train = []
predict_train = []
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
cost_average, simi, predict = train_model(batch_start)
simi_train.append(simi)
predict_train.append(predict)
if iter % 1000 == 0:
f.write('@iter :' + str(iter) + '\n')
if iter % n_train_batches == 0:
corr_train = compute_corr_train(predict_train, _train_Label)
res = 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate: ' + str(
corr_train * 100.0 / train_size) + '\n'
f.write(res)
if iter % validation_frequency == 0 or iter % 20000 == 0:
posi_test_sent = []
nega_test_sent = []
posi_test_doc = []
nega_test_doc = []
posi_test_overall = []
nega_test_overall = []
simi_test = []
predict_test = []
for i in test_batch_start:
cost, simi, predict = test_model(i)
#print simi
#f.write('test_predict : ' + str(predict) + ' test_simi : ' + str(simi) + '\n' )
simi_test.append(simi)
predict_test.append(predict)
corr_test = compute_corr(simi_test, predict_test, f)
test_acc = corr_test * 1.0 / (test_size / 4.0)
res = '\t\t\tepoch ' + str(epoch) + ', minibatch ' + str(
minibatch_index) + ' / ' + str(
n_train_batches) + ' test acc of best model ' + str(
test_acc * 100.0) + '\n'
f.write(res)
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
res = '\t\t\tmax: ' + str(max_acc) + ' (at ' + str(
best_epoch) + ')\n'
f.write(res)
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, nkerns=[50], batch_size=10, window_width=3,
maxSentLength=1050, emb_size=50, hidden_size=200,
margin=0.5):
#def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, nkerns=[6, 12], batch_size=70, useAllSamples=0, kmax=30, ktop=5, filter_size=[10,7],
# L2_weight=0.000005, dropout_p=0.5, useEmb=0, task=5, corpus=1):
ibmPath='/mounts/data/proj/wenpeng/Dataset/insuranceQA/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_ibm_corpus(ibmPath+'vocabulary', ibmPath+'train.txt', ibmPath+'dev.txt', maxSentLength)
indices_train, trainLengths, trainLeftPad, trainRightPad= datasets[0]
#print trainY.eval().shape[0]
indices_dev, devY, devLengths, devLeftPad, devRightPad= datasets[1]
n_train_batches=indices_train.shape[0]/(batch_size*4) #note that we consider 4 lines as an example in training
n_valid_batches=indices_dev.shape[0]/(batch_size*4)
train_batch_start=list(numpy.arange(n_train_batches)*(batch_size*4))
dev_batch_start=list(numpy.arange(n_valid_batches)*(batch_size*4))
indices_train_theano=theano.shared(numpy.asarray(indices_train, dtype=theano.config.floatX), borrow=True)
indices_dev_theano=theano.shared(numpy.asarray(indices_dev, dtype=theano.config.floatX), borrow=True)
indices_train_theano=T.cast(indices_train_theano, 'int32')
indices_dev_theano=T.cast(indices_dev_theano, 'int32')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(1e-50+numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values)
embeddings=theano.shared(value=rand_values)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x_index = T.imatrix('x_index') # now, x is the index matrix, must be integer
#left=T.ivector('left')
#right=T.ivector('right')
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_input = embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0 = Conv(rng, input=layer0_input,
image_shape=((batch_size*4), 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
layer0_out=debug_print(layer0.output, 'layer0_out')
layer1=Average_Pooling(rng, input=layer0_out, length_last_dim=length_after_wideConv, kern=nkerns[0] )
layer1_out=debug_print(layer1.output.reshape((batch_size*2, nkerns[0]*2)), 'layer1_out')
layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
layer3=HiddenLayer(rng, input=layer2.output, n_in=hidden_size, n_out=1, activation=T.tanh)
posi_score=layer3.output[0:layer3.output.shape[0]:2,:]
nega_score=layer3.output[1:layer3.output.shape[0]:2,:]
cost=T.maximum(0, margin-T.sum(posi_score-nega_score))
#cost = layer3.negative_log_likelihood(y)
# output a list of score
dev_model = theano.function([index], layer3.output.flatten(),
givens={
x_index: indices_dev_theano[index: index + (batch_size*4)]})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params+layer0.params
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index], [cost], updates=updates,
givens={
x_index: indices_train_theano[index: index + (batch_size*4)]})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches/5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
cost_ij= train_model(batch_start)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' cost: '+str(cost_ij)
if iter % validation_frequency == 0:
dev_scores=[]
for i in dev_batch_start:
dev_scores+=list(dev_model(i))
acc_dev=compute_acc(devY, dev_scores)
print(('\t\t\t\tepoch %i, minibatch %i/%i, dev acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
acc_dev * 100.))
'''
#print 'validating & testing...'
# compute zero-one loss on validation set
validation_losses = []
for i in dev_batch_start:
time.sleep(0.5)
validation_losses.append(validate_model(i))
#validation_losses = [validate_model(i) for i in dev_batch_start]
this_validation_loss = numpy.mean(validation_losses)
print('\t\tepoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index , n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in test_batch_start]
test_score = numpy.mean(test_losses)
print(('\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self,
learning_rate=0.2,
n_epochs=2000,
nkerns=[6, 14],
batch_size=10,
useAllSamples=0,
ktop=4,
filter_size=[7, 5],
L2_weight=0.00005,
dropout_p=0.8,
useEmb=0,
task=2,
corpus=1,
dataMode=3,
maxSentLength=60,
sentEm_length=48,
window=3,
k=5,
nce_seeds=2345,
only_left_context=False,
vali_cost_list_length=20,
embedding_size=48,
train_scheme=1):
self.ini_learning_rate = learning_rate
self.n_epochs = n_epochs
self.nkerns = nkerns
self.batch_size = batch_size
self.useAllSamples = useAllSamples
self.ktop = ktop
self.filter_size = filter_size
self.L2_weight = L2_weight
self.dropout_p = dropout_p
self.useEmb = useEmb
self.task = task
self.corpus = corpus
self.dataMode = dataMode
self.maxSentLength = maxSentLength
self.kmax = self.maxSentLength / 2 + 5
self.sentEm_length = sentEm_length
self.window = window
self.k = k
self.only_left_context = only_left_context
if self.only_left_context:
self.context_size = self.window
else:
self.context_size = 2 * self.window
self.nce_seed = nce_seeds
self.embedding_size = 0
self.train_scheme = train_scheme
root = "/mounts/data/proj/wenpeng/Dataset/StanfordSentiment/stanfordSentimentTreebank/"
wiki_path = "/mounts/data/proj/wenpeng/PhraseEmbedding/enwiki-20130503-pages-articles-cleaned-tokenized"
embeddingPath = '/mounts/data/proj/wenpeng/Downloads/hlbl-embeddings-original.EMBEDDING_SIZE=50.txt'
embeddingPath2 = '/mounts/data/proj/wenpeng/MC/src/released_embedding.txt'
datasets, unigram, train_lengths, dev_lengths, word_count = load_model_for_training(
wiki_path, root + str(self.task) + 'classes/' + str(self.corpus) +
'train.txt',
root + str(self.task) + 'classes/' + str(self.corpus) + 'dev.txt',
self.maxSentLength, self.dataMode, self.train_scheme)
self.datasets = datasets
self.embedding_size = embedding_size
self.vocab_size = word_count
rand_values = random_value_normal(
(self.vocab_size + 1, self.embedding_size), theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(self.embedding_size))
self.embeddings_R = theano.shared(value=rand_values)
rand_values = random_value_normal(
(self.vocab_size + 1, self.embedding_size), theano.config.floatX,
numpy.random.RandomState(4321))
rand_values[0] = numpy.array(numpy.zeros(self.embedding_size))
self.embeddings_Q = theano.shared(value=rand_values)
self.unigram = unigram
self.p_n = theano.shared(value=self.unigram)
self.train_lengths = train_lengths
self.dev_lengths = dev_lengths
b_values = zero_value((len(unigram), ), dtype=theano.config.floatX)
self.bias = theano.shared(value=b_values, name='bias')
self.vali_cost_list_length = vali_cost_list_length
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59,
margin=0.2):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus_DSSSS(
rootPath + 'vocab_DSSSS.txt', rootPath +
'mc500.train.tsv_standardlized.txt_with_state.txt_DSSSS.txt',
rootPath + 'mc500.test.tsv_standardlized.txt_with_state.txt_DSSSS.txt',
max_s_length, maxSentLength,
maxDocLength) #vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[
train_data_D, train_data_A1, train_data_A2, train_data_A3,
train_data_A4, train_Label, train_Length_D, train_Length_D_s,
train_Length_A1, train_Length_A2, train_Length_A3, train_Length_A4,
train_leftPad_D, train_leftPad_D_s, train_leftPad_A1, train_leftPad_A2,
train_leftPad_A3, train_leftPad_A4, train_rightPad_D,
train_rightPad_D_s, train_rightPad_A1, train_rightPad_A2,
train_rightPad_A3, train_rightPad_A4
] = train_data
[
test_data_D, test_data_A1, test_data_A2, test_data_A3, test_data_A4,
test_Label, test_Length_D, test_Length_D_s, test_Length_A1,
test_Length_A2, test_Length_A3, test_Length_A4, test_leftPad_D,
test_leftPad_D_s, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3,
test_leftPad_A4, test_rightPad_D, test_rightPad_D_s, test_rightPad_A1,
test_rightPad_A2, test_rightPad_A3, test_rightPad_A4
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1 = T.lvector()
index_A2 = T.lvector()
index_A3 = T.lvector()
index_A4 = T.lvector()
# y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
# len_Q=T.lscalar()
len_A1 = T.lscalar()
len_A2 = T.lscalar()
len_A3 = T.lscalar()
len_A4 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
# left_Q=T.lscalar()
left_A1 = T.lscalar()
left_A2 = T.lscalar()
left_A3 = T.lscalar()
left_A4 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
# right_Q=T.lscalar()
right_A1 = T.lscalar()
right_A2 = T.lscalar()
right_A3 = T.lscalar()
right_A4 = T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_Q_input = embeddings[index_Q.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(rng, nkerns[0], nkerns[1])
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A2 = Conv_with_input_para(
rng,
input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A3 = Conv_with_input_para(
rng,
input=layer0_A3_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A4 = Conv_with_input_para(
rng,
input=layer0_A4_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
layer0_A2_output = debug_print(layer0_A2.output, 'layer0_A2.output')
layer0_A3_output = debug_print(layer0_A3.output, 'layer0_A3.output')
layer0_A4_output = debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA2 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A2_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A2,
right_r=right_A2,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A2 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA3 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A3_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A3,
right_r=right_A3,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A3 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA4 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A4_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A4,
right_r=right_A4,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A4 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA2 = Conv_with_input_para(
rng,
input=layer1_DA2.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA3 = Conv_with_input_para(
rng,
input=layer1_DA3.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA4 = Conv_with_input_para(
rng,
input=layer1_DA4.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A2 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA2.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A3 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA3.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A4 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA4.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer2_A2_output_sent_rep_Dlevel = debug_print(
layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
layer2_A3_output_sent_rep_Dlevel = debug_print(
layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
layer2_A4_output_sent_rep_Dlevel = debug_print(
layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA2 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA2.output,
input_r=layer2_A2_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA3 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA3.output,
input_r=layer2_A3_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA4 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA4.output,
input_r=layer2_A4_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_DA2 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA2.output_D_sent_level_rep) + high_b),
'transform_gate_DA2')
transform_gate_DA3 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA3.output_D_sent_level_rep) + high_b),
'transform_gate_DA3')
transform_gate_DA4 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA4.output_D_sent_level_rep) + high_b),
'transform_gate_DA4')
# transform_gate_Q=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b), 'transform_gate_Q')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
transform_gate_A2 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b),
'transform_gate_A2')
transform_gate_A3 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b),
'transform_gate_A3')
transform_gate_A4 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b),
'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
overall_D_A2 = (
1.0 - transform_gate_DA2
) * layer1_DA2.output_D_sent_level_rep + transform_gate_DA2 * layer3_DA2.output_D_doc_level_rep
overall_D_A3 = (
1.0 - transform_gate_DA3
) * layer1_DA3.output_D_sent_level_rep + transform_gate_DA3 * layer3_DA3.output_D_doc_level_rep
overall_D_A4 = (
1.0 - transform_gate_DA4
) * layer1_DA4.output_D_sent_level_rep + transform_gate_DA4 * layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
overall_A2 = (
1.0 - transform_gate_A2
) * layer1_DA2.output_QA_sent_level_rep + transform_gate_A2 * layer2_A2.output_sent_rep_Dlevel
overall_A3 = (
1.0 - transform_gate_A3
) * layer1_DA3.output_QA_sent_level_rep + transform_gate_A3 * layer2_A3.output_sent_rep_Dlevel
overall_A4 = (
1.0 - transform_gate_A4
) * layer1_DA4.output_QA_sent_level_rep + transform_gate_A4 * layer2_A4.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_sent_level2 = debug_print(
cosine(layer1_DA2.output_D_sent_level_rep,
layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
simi_sent_level3 = debug_print(
cosine(layer1_DA3.output_D_sent_level_rep,
layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
simi_sent_level4 = debug_print(
cosine(layer1_DA4.output_D_sent_level_rep,
layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_doc_level2 = debug_print(
cosine(layer3_DA2.output_D_doc_level_rep,
layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
simi_doc_level3 = debug_print(
cosine(layer3_DA3.output_D_doc_level_rep,
layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
simi_doc_level4 = debug_print(
cosine(layer3_DA4.output_D_doc_level_rep,
layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
simi_overall_level2 = debug_print(cosine(overall_D_A2, overall_A2),
'simi_overall_level2')
simi_overall_level3 = debug_print(cosine(overall_D_A3, overall_A3),
'simi_overall_level3')
simi_overall_level4 = debug_print(cosine(overall_D_A4, overall_A4),
'simi_overall_level4')
simi_1 = simi_overall_level1 #+simi_sent_level1+simi_doc_level1
simi_2 = simi_overall_level2 #+simi_sent_level2+simi_doc_level2
simi_3 = simi_overall_level3 #+simi_sent_level3+simi_doc_level3
simi_4 = simi_overall_level4 #+simi_sent_level4+simi_doc_level4
# simi_1=(simi_overall_level1+simi_sent_level1+simi_doc_level1)/3.0
# simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)+T.maximum(0.0, margin+simi_3-simi_1)+T.maximum(0.0, margin+simi_4-simi_1)
cost12 = T.maximum(
0.0, margin + simi_sent_level2 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level2 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level2 - simi_overall_level1)
cost13 = T.maximum(
0.0, margin + simi_sent_level3 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level3 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level3 - simi_overall_level1)
cost14 = T.maximum(
0.0, margin + simi_sent_level4 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level4 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level4 - simi_overall_level1)
cost = cost12 + cost13 + cost14
posi_simi = T.max([simi_sent_level1, simi_doc_level1, simi_overall_level1])
nega_simi = T.max([
simi_sent_level2, simi_doc_level2, simi_overall_level2,
simi_sent_level3, simi_doc_level3, simi_overall_level3,
simi_sent_level4, simi_doc_level4, simi_overall_level4
])
L2_reg = debug_print(
(high_W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum(), 'L2_reg'
) #+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost = debug_print(cost + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
corr_train = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
((iter % train_size) * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
cost_average, posi_simi, nega_simi = train_model(batch_start)
if posi_simi > nega_simi:
corr_train += 1
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate:' + str(
corr_train * 100.0 / train_size)
if iter % validation_frequency == 0:
corr_test = 0
for i in test_batch_start:
cost, posi_simi, nega_simi = test_model(i)
if posi_simi > nega_simi:
corr_test += 1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = corr_test * 1.0 / test_size
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.08, n_epochs=2000, nkerns=[44], batch_size=1, window_width=3,
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0006, update_freq=1, norm_threshold=5.0, max_truncate=24):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SICK/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_SICK_corpus(rootPath+'vocab_nonoverlap.txt', rootPath+'train_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate,maxSentLength, entailment=True)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
extra_train, extra_test=load_extra_features(rootPath+'train_rule_features_cosine_eucli_negation_len1_len2.txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2.txt')
discri_train, discri_test=load_extra_features(rootPath+'train_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_nonoverlap_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
extra=T.dmatrix()
discri=T.dmatrix()
#wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
linear=Linear(norm_uni_l, norm_uni_r)
poly=Poly(norm_uni_l, norm_uni_r)
sigmoid=Sigmoid(norm_uni_l, norm_uni_r)
rbf=RBF(norm_uni_l, norm_uni_r)
gesd=GESD(norm_uni_l, norm_uni_r)
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([mts,
eucli_1,uni_cosine,#linear, poly,sigmoid,rbf, gesd, #sum_uni_r-sum_uni_l,
layer1.output_eucli_to_simi,layer1.output_cosine, #layer1.output_vector_r-layer1.output_vector_l,
len_l, len_r,
extra
#discri
#wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=14+(2)+(2)+2+5, n_out=3)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.errors(y),layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
extra: extra_test[index: index + batch_size],
discri:discri_test[index: index + batch_size]
#wmf: wm_test[index: index + batch_size]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params
params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
for i in test_batch_start:
test_loss, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
#lr=LinearRegression().fit(train_features, train_y)
#results_lr=lr.predict(test_features)
corr_count=0
#corr_lr=0
corr_neu=0
neu_co=0
corr_ent=0
ent_co=0
corr_contr=0
contr_co=0
test_size=len(test_y)
for i in range(test_size):
if test_y[i]==0:#NEUTRAL
neu_co+=1
if results[i]==test_y[i]:
corr_neu+=1
elif test_y[i]==1:#ENTAILMENT
ent_co+=1
if results[i]==test_y[i]:
corr_ent+=1
elif test_y[i]==2:#CONTRADICTION
contr_co+=1
if results[i]==test_y[i]:
corr_contr+=1
'''
if results[i]==test_y[i]:
corr_count+=1
if test_y[i]==0: #NEUTRAL
corr_neu+=1
'''
#if numpy.absolute(results_lr[i]-test_y[i])<0.5:
# corr_lr+=1
corr_count=corr_neu+corr_ent+corr_contr
acc=corr_count*1.0/test_size
acc_neu=corr_neu*1.0/neu_co
acc_ent=corr_ent*1.0/ent_co
acc_contr=corr_contr*1.0/contr_co
#acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_epoch=epoch
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
#if acc_lr> max_acc:
# max_acc=acc_lr
# best_epoch=epoch
print '\t\t\tsvm acc: ', acc, ' max acc: ', max_acc,'(at',best_epoch,')',' Neu: ',acc_neu, ' Ent: ',acc_ent, ' Contr: ',acc_contr
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1,
n_epochs=2000,
nkerns=[50],
batch_size=10,
window_width=3,
maxSentLength=1050,
emb_size=50,
hidden_size=200,
margin=0.5):
#def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, nkerns=[6, 12], batch_size=70, useAllSamples=0, kmax=30, ktop=5, filter_size=[10,7],
# L2_weight=0.000005, dropout_p=0.5, useEmb=0, task=5, corpus=1):
ibmPath = '/mounts/data/proj/wenpeng/Dataset/insuranceQA/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_ibm_corpus(ibmPath + 'vocabulary',
ibmPath + 'train.txt',
ibmPath + 'dev.txt', maxSentLength)
indices_train, trainLengths, trainLeftPad, trainRightPad = datasets[0]
#print trainY.eval().shape[0]
indices_dev, devY, devLengths, devLeftPad, devRightPad = datasets[1]
n_train_batches = indices_train.shape[0] / (
batch_size * 4
) #note that we consider 4 lines as an example in training
n_valid_batches = indices_dev.shape[0] / (batch_size * 4)
train_batch_start = list(numpy.arange(n_train_batches) * (batch_size * 4))
dev_batch_start = list(numpy.arange(n_valid_batches) * (batch_size * 4))
indices_train_theano = theano.shared(numpy.asarray(
indices_train, dtype=theano.config.floatX),
borrow=True)
indices_dev_theano = theano.shared(numpy.asarray(
indices_dev, dtype=theano.config.floatX),
borrow=True)
indices_train_theano = T.cast(indices_train_theano, 'int32')
indices_dev_theano = T.cast(indices_dev_theano, 'int32')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(1e-50 + numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values)
embeddings = theano.shared(value=rand_values)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x_index = T.imatrix(
'x_index') # now, x is the index matrix, must be integer
#left=T.ivector('left')
#right=T.ivector('right')
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_input = embeddings[x_index.flatten()].reshape(
((batch_size * 4), maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0 = Conv(rng,
input=layer0_input,
image_shape=((batch_size * 4), 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
layer0_out = debug_print(layer0.output, 'layer0_out')
layer1 = Average_Pooling(rng,
input=layer0_out,
length_last_dim=length_after_wideConv,
kern=nkerns[0])
layer1_out = debug_print(
layer1.output.reshape((batch_size * 2, nkerns[0] * 2)), 'layer1_out')
layer2 = HiddenLayer(rng,
input=layer1_out,
n_in=nkerns[0] * 2,
n_out=hidden_size,
activation=T.tanh)
layer3 = HiddenLayer(rng,
input=layer2.output,
n_in=hidden_size,
n_out=1,
activation=T.tanh)
posi_score = layer3.output[0:layer3.output.shape[0]:2, :]
nega_score = layer3.output[1:layer3.output.shape[0]:2, :]
cost = T.maximum(0, margin - T.sum(posi_score - nega_score))
#cost = layer3.negative_log_likelihood(y)
# output a list of score
dev_model = theano.function(
[index],
layer3.output.flatten(),
givens={x_index: indices_dev_theano[index:index + (batch_size * 4)]})
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i /
(T.sqrt(acc) + 1e-20))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index], [cost],
updates=updates,
givens={x_index: indices_train_theano[index:index + (batch_size * 4)]})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches / 5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
cost_ij = train_model(batch_start)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(iter) + ' cost: ' + str(
cost_ij)
if iter % validation_frequency == 0:
dev_scores = []
for i in dev_batch_start:
dev_scores += list(dev_model(i))
acc_dev = compute_acc(devY, dev_scores)
print(
('\t\t\t\tepoch %i, minibatch %i/%i, dev acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, acc_dev * 100.))
'''
#print 'validating & testing...'
# compute zero-one loss on validation set
validation_losses = []
for i in dev_batch_start:
time.sleep(0.5)
validation_losses.append(validate_model(i))
#validation_losses = [validate_model(i) for i in dev_batch_start]
this_validation_loss = numpy.mean(validation_losses)
print('\t\tepoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index , n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in test_batch_start]
test_score = numpy.mean(test_losses)
print(('\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.08, n_epochs=2000, nkerns=[50], batch_size=1000, window_width=4,
maxSentLength=64, emb_size=50, hidden_size=50,
margin=0.5, L2_weight=0.0004, update_freq=1, norm_threshold=5.0, max_truncate=40, line_no=483142, comment='v5_margin0.6_neg300_'):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
triple_path='/mounts/data/proj/wenpeng/Dataset/freebase/FB15k/'
rng = numpy.random.RandomState(1234)
triples, entity_size, relation_size, train_triples_set, train_entity_set, train_relation_set,dev_triples, dev_triples_set, dev_entity_set, dev_relation_set, test_triples, test_triples_set, test_entity_set, test_relation_set=load_TrainDevTest_triples_RankingLoss(triple_path+'freebase_mtr100_mte100-train.txt',triple_path+'freebase_mtr100_mte100-valid.txt', triple_path+'freebase_mtr100_mte100-test.txt', line_no, triple_path)
print 'triple size:', len(triples), 'entity_size:', entity_size, 'relation_size:', relation_size#, len(entity_count), len(relation_count)
dev_size=len(dev_triples)
print 'dev triple size:', dev_size, 'entity_size:', len(dev_entity_set)
test_size=len(test_triples)
print 'test triple size:', test_size, 'entity_size:', len(test_entity_set)
# print triples
# print entity_count
# print relation_count
# exit(0)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
# mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
# wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# entity_count=theano.shared(numpy.asarray(entity_count, dtype=theano.config.floatX), borrow=True)
# entity_count=T.cast(entity_count, 'int64')
# relation_count=theano.shared(numpy.asarray(relation_count, dtype=theano.config.floatX), borrow=True)
# relation_count=T.cast(relation_count, 'int64')
rand_values=random_value_normal((entity_size, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
entity_E=theano.shared(value=rand_values, borrow=True)
rand_values=random_value_normal((relation_size, emb_size), theano.config.floatX, numpy.random.RandomState(4321))
relation_E=theano.shared(value=rand_values, borrow=True)
GRU_U, GRU_W, GRU_b=create_GRU_para(rng, word_dim=emb_size, hidden_dim=emb_size)
# GRU_U_combine, GRU_W_combine, GRU_b_combine=create_nGRUs_para(rng, word_dim=emb_size, hidden_dim=emb_size, n=3)
para_to_load=[entity_E, relation_E, GRU_U, GRU_W, GRU_b]
load_model_from_file(triple_path+comment+'Best_Paras_dim'+str(emb_size), para_to_load)
norm_entity_E=norm_matrix(entity_E)
norm_relation_E=norm_matrix(relation_E)
n_batchs=line_no/batch_size
remain_triples=line_no%batch_size
if remain_triples>0:
batch_start=list(numpy.arange(n_batchs)*batch_size)+[line_no-batch_size]
else:
batch_start=list(numpy.arange(n_batchs)*batch_size)
batch_start=theano.shared(numpy.asarray(batch_start, dtype=theano.config.floatX), borrow=True)
batch_start=T.cast(batch_start, 'int64')
test_triple = T.lvector('test_triple')
neg_inds = T.lvector('neg_inds')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
predicted_tail=GRU_Combine_2Vector(norm_entity_E[test_triple[0]], norm_relation_E[test_triple[1]], emb_size, GRU_U, GRU_W, GRU_b)
golden_tail=norm_entity_E[test_triple[2]]
pos_loss=(1-cosine(predicted_tail,golden_tail))**2
neg_Es=norm_entity_E[neg_inds].reshape((neg_inds.shape[0], emb_size))
predicted_tail=predicted_tail.reshape((1, emb_size))
multi=T.sum(predicted_tail*neg_Es, axis=1)
len1=T.sqrt(T.sum(predicted_tail**2))
len2=T.sqrt(T.sum(neg_Es**2, axis=1))
cos=multi/(len1*len2)
neg_loss_vector=(1-cos)**2
# normed_predicted_tail=predicted_tail/T.sqrt(T.sum(predicted_tail**2))
#
# pos_loss=T.sum(abs(normed_predicted_tail-golden_tail))
# neg_Es=norm_entity_E[neg_inds].reshape((neg_inds.shape[0], emb_size))
# predicted_tail=normed_predicted_tail.reshape((1, emb_size))
#
# neg_loss_vector=T.sum(abs(predicted_tail-neg_Es), axis=1)
GRU_forward_step = theano.function([test_triple, neg_inds], [pos_loss,neg_loss_vector], on_unused_input='ignore')
#
# train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
# givens={
# x_index_l: indices_train_l[index: index + batch_size],
# x_index_r: indices_train_r[index: index + batch_size],
# y: trainY[index: index + batch_size],
# left_l: trainLeftPad_l[index],
# right_l: trainRightPad_l[index],
# left_r: trainLeftPad_r[index],
# right_r: trainRightPad_r[index],
# length_l: trainLengths_l[index],
# length_r: trainLengths_r[index],
# norm_length_l: normalized_train_length_l[index],
# norm_length_r: normalized_train_length_r[index],
# mts: mt_train[index: index + batch_size],
# wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
# validation_frequency = min(n_train_batches/5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
corpus_triples_set=train_triples_set|dev_triples_set|test_triples_set
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
# cost_1, cost_l= train_model(triples)
# #print 'layer3_input', layer3_input
# print 'cost:', cost_1, cost_l
#test
test_size=len(test_triples)
hits_10=test_size
hits_1=test_size
co=0
for test_triple in test_triples:
co+=1
count=0
flag_continue=True
nega_entity_set=get_negas(test_triple, corpus_triples_set, test_entity_set)
# print len(nega_entity_set)
p_loss, n_loss_vector=GRU_forward_step(test_triple, list(nega_entity_set))
n_loss_vector=numpy.sort(n_loss_vector)
# print p_loss
# print n_loss_vector[:15]
# exit(0)
if p_loss>n_loss_vector[0]:
hits_1-=1
if p_loss>n_loss_vector[9]:
hits_10-=1
if co%1000==0:
print co, '...'
print '\t\thits_10', hits_10*100.0/test_size, 'hits_1', hits_1*100.0/test_size
hits_10=hits_10*100.0/test_size
hits_1=hits_1*100.0/test_size
# if patience <= iter:
# done_looping = True
# break
#after each epoch, increase the batch_size
store_model_to_file(triple_path+'Best_Paras_dim'+str(emb_size)+'_hits10_'+str(hits_10)[:6], para_to_load)
print 'Finished storing best params'
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min, Hits_10:', hits_10, 'Hits_1:,', hits_1
mid_time = time.clock()
exit(0)
# exit(0)
# #store the paras after epoch 15
# if epoch ==22:
# store_model_to_file(params_conv)
# print 'Finished storing best conv params'
# exit(0)
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, batch_size=10000, emb_size=50,
margin=0.3, L2_weight=1e-10, update_freq=1, norm_threshold=5.0, max_truncate=40, line_no=16450007, neg_size=60, test_neg_size=300,
comment=''):#L1Distance_
model_options = locals().copy()
print "model options", model_options
triple_path='/mounts/data/proj/wenpeng/Dataset/freebase/SimpleQuestions_v2/freebase-subsets/'
rng = numpy.random.RandomState(1234)
# triples, entity_size, relation_size, entity_count, relation_count=load_triples(triple_path+'freebase_mtr100_mte100-train.txt', line_no, triple_path)#vocab_size contain train, dev and test
triples, entity_size, relation_size, train_triples_set, train_entity_set, train_relation_set,statistics=load_Train(triple_path+'freebase-FB5M2M-combined.txt', line_no, triple_path)
train_h2t=statistics[0]
train_t2h=statistics[1]
train_r2t=statistics[2]
train_r2h=statistics[3]
train_r_replace_tail_prop=statistics[4]
print 'triple size:', len(triples), 'entity_size:', entity_size, 'relation_size:', relation_size#, len(entity_count), len(relation_count)
rand_values=random_value_normal((entity_size, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
entity_E=theano.shared(value=rand_values, borrow=True)
rand_values=random_value_normal((relation_size, emb_size), theano.config.floatX, numpy.random.RandomState(4321))
relation_E=theano.shared(value=rand_values, borrow=True)
GRU_U, GRU_W, GRU_b=create_GRU_para(rng, word_dim=emb_size, hidden_dim=emb_size)
# GRU_U1, GRU_W1, GRU_b1=create_GRU_para(rng, word_dim=emb_size, hidden_dim=emb_size)
# GRU_U2, GRU_W2, GRU_b2=create_GRU_para(rng, word_dim=emb_size, hidden_dim=emb_size)
# GRU_U_combine, GRU_W_combine, GRU_b_combine=create_nGRUs_para(rng, word_dim=emb_size, hidden_dim=emb_size, n=3)
# para_to_load=[entity_E, relation_E, GRU_U, GRU_W, GRU_b]
# load_model_from_file(triple_path+'Best_Paras_dim'+str(emb_size), para_to_load) #+'_hits10_63.616'
# GRU_U_combine=[GRU_U0, GRU_U1, GRU_U2]
# GRU_W_combine=[GRU_W0, GRU_W1, GRU_W2]
# GRU_b_combine=[GRU_b0, GRU_b1, GRU_b2]
# w2v_entity_rand_values=random_value_normal((entity_size, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
#
# w2v_relation_rand_values=random_value_normal((relation_size, emb_size), theano.config.floatX, numpy.random.RandomState(4321))
#
# w2v_entity_rand_values=load_word2vec_to_init(w2v_entity_rand_values, triple_path+'freebase_mtr100_mte100-train.txt_ids_entityEmb50.txt')
# w2v_relation_rand_values=load_word2vec_to_init(w2v_relation_rand_values, triple_path+'freebase_mtr100_mte100-train.txt_ids_relationEmb50.txt')
# w2v_entity_rand_values=theano.shared(value=w2v_entity_rand_values, borrow=True)
# w2v_relation_rand_values=theano.shared(value=w2v_relation_rand_values, borrow=True)
# entity_E_ensemble=entity_E+norm_matrix(w2v_entity_rand_values)
# relation_E_ensemble=relation_E+norm_matrix(w2v_relation_rand_values)
norm_entity_E=norm_matrix(entity_E)
norm_relation_E=norm_matrix(relation_E)
n_batchs=line_no/batch_size
remain_triples=line_no%batch_size
if remain_triples>0:
batch_start=list(numpy.arange(n_batchs)*batch_size)+[line_no-batch_size]
else:
batch_start=list(numpy.arange(n_batchs)*batch_size)
# batch_start=theano.shared(numpy.asarray(batch_start, dtype=theano.config.floatX), borrow=True)
# batch_start=T.cast(batch_start, 'int64')
# allocate symbolic variables for the data
# index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
n_index_T = T.ltensor3('n_index_T')
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
dist_tail=one_batch_parallel_Ramesh(x_index_l, norm_entity_E, norm_relation_E, GRU_U, GRU_W, GRU_b, emb_size)
loss__tail_is=one_neg_batches_parallel_Ramesh(n_index_T, norm_entity_E, norm_relation_E, GRU_U, GRU_W, GRU_b, emb_size)
loss_tail_i=T.maximum(0.0, margin+dist_tail.reshape((dist_tail.shape[0],1))-loss__tail_is)
# loss_relation_i=T.maximum(0.0, margin+dist_relation.reshape((dist_relation.shape[0],1))-loss_relation_is)
# loss_head_i=T.maximum(0.0, margin+dist_head.reshape((dist_head.shape[0],1))-loss_head_is)
# loss_tail_i_test=T.maximum(0.0, 0.0+dist_tail.reshape((dist_tail.shape[0],1))-loss__tail_is)
# binary_matrix_test=T.gt(loss_tail_i_test, 0)
# sum_vector_test=T.sum(binary_matrix_test, axis=1)
# binary_vector_hits10=T.gt(sum_vector_test, 10)
# test_loss=T.sum(binary_vector_hits10)*1.0/batch_size
# loss_relation_i=T.maximum(0.0, margin+dis_relation.reshape((dis_relation.shape[0],1))-loss__relation_is)
# loss_head_i=T.maximum(0.0, margin+dis_head.reshape((dis_head.shape[0],1))-loss__head_is)
# def neg_slice(neg_matrix):
# dist_tail_slice, dis_relation_slice, dis_head_slice=one_batch_parallel_Ramesh(neg_matrix, entity_E, relation_E, GRU_U_combine, GRU_W_combine, GRU_b_combine, emb_size)
# loss_tail_i=T.maximum(0.0, margin+dist_tail-dist_tail_slice)
# loss_relation_i=T.maximum(0.0, margin+dis_relation-dis_relation_slice)
# loss_head_i=T.maximum(0.0, margin+dis_head-dis_head_slice)
# return loss_tail_i, loss_relation_i, loss_head_i
#
# (loss__tail_is, loss__relation_is, loss__head_is), updates = theano.scan(
# neg_slice,
# sequences=n_index_T,
# outputs_info=None)
loss_tails=T.mean(T.sum(loss_tail_i, axis=1) )
# loss_relations=T.mean(T.sum(loss_relation_i, axis=1) )
# loss_heads=T.mean(T.sum(loss_head_i, axis=1) )
loss=loss_tails#+loss_relations+loss_heads
L2_loss=debug_print((entity_E** 2).sum()+(relation_E** 2).sum()\
+(GRU_U** 2).sum()+(GRU_W** 2).sum(), 'L2_reg')
# Div_loss=Diversify_Reg(GRU_U[0])+Diversify_Reg(GRU_U[1])+Diversify_Reg(GRU_U[2])+\
# Diversify_Reg(GRU_W[0])+Diversify_Reg(GRU_W[1])+Diversify_Reg(GRU_W[2])
cost=loss+L2_weight*L2_loss#+div_reg*Div_loss
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = [entity_E, relation_E, GRU_U, GRU_W, GRU_b]
# params_conv = [conv_W, conv_b]
params_to_store=[entity_E, relation_E, GRU_U, GRU_W, GRU_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc+1e-9))) #AdaGrad
updates.append((acc_i, acc))
# grads = T.grad(cost, params)
# updates = []
# for param_i, grad_i in zip(params, grads):
# updates.append((param_i, param_i - learning_rate * grad_i)) #AdaGrad
train_model = theano.function([x_index_l, n_index_T], [loss, cost], updates=updates,on_unused_input='ignore')
# test_model = theano.function([x_index_l, n_index_T], test_loss, on_unused_input='ignore')
#
# train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
# givens={
# x_index_l: indices_train_l[index: index + batch_size],
# x_index_r: indices_train_r[index: index + batch_size],
# y: trainY[index: index + batch_size],
# left_l: trainLeftPad_l[index],
# right_l: trainRightPad_l[index],
# left_r: trainLeftPad_r[index],
# right_r: trainRightPad_r[index],
# length_l: trainLengths_l[index],
# length_r: trainLengths_r[index],
# norm_length_l: normalized_train_length_l[index],
# norm_length_r: normalized_train_length_r[index],
# mts: mt_train[index: index + batch_size],
# wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
# validation_frequency = min(n_train_batches/5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
# corpus_triples_set=train_triples_set|dev_triples_set|test_triples_set
best_train_loss=1000000
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
# learning_rate/=epoch
# print 'lr:', learning_rate
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
loss_sum=0.0
for start in batch_start:
if start%100000==0:
print start, '...'
pos_triples=triples[start:start+batch_size]
all_negs=[]
# count=0
for pos_triple in pos_triples:
neg_triples=get_n_neg_triples_train(pos_triple, train_triples_set, train_entity_set, train_r_replace_tail_prop, neg_size)
# # print 'neg_head_triples'
# neg_relation_triples=get_n_neg_triples(pos_triple, train_triples_set, train_entity_set, train_relation_set, 1, neg_size/3)
# # print 'neg_relation_triples'
# neg_tail_triples=get_n_neg_triples(pos_triple, train_triples_set, train_entity_set, train_relation_set, 2, neg_size/3)
# print 'neg_tail_triples'
all_negs.append(neg_triples)
# print 'neg..', count
# count+=1
neg_tensor=numpy.asarray(all_negs).reshape((batch_size, neg_size, 3)).transpose(1,0,2)
loss, cost= train_model(pos_triples, neg_tensor)
loss_sum+=loss
loss_sum/=len(batch_start)
print 'Training loss:', loss_sum, 'cost:', cost
# loss_test=0.0
#
# for test_start in batch_start_test:
# pos_triples=test_triples[test_start:test_start+batch_size]
# all_negs=[]
# for pos_triple in pos_triples:
# neg_triples=get_n_neg_triples_new(pos_triple, corpus_triples_set, test_entity_set, test_relation_set, test_neg_size/2, True)
# all_negs.append(neg_triples)
#
# neg_tensor=numpy.asarray(all_negs).reshape((batch_size, test_neg_size, 3)).transpose(1,0,2)
# loss_test+= test_model(pos_triples, neg_tensor)
#
#
# loss_test/=n_batchs_test
# print '\t\t\tUpdating epoch', epoch, 'finished! Test hits10:', 1.0-loss_test
if loss_sum< best_train_loss:
store_model_to_file(triple_path+comment+'Best_Paras_dim'+str(emb_size), params_to_store)
# store_model_to_file(triple_path+'Divreg_Best_Paras_dim'+str(emb_size), params_to_store)
best_train_loss=loss_sum
print 'Finished storing best params'
# exit(0)
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.09, n_epochs=2000, nkerns=[50], batch_size=1, window_width=3,
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.00065, Div_reg=0.01, update_freq=1, norm_threshold=5.0, max_truncate=33, max_truncate_nonoverlap=24):
maxSentLength=max_truncate+2*(window_width-1)
maxSentLength_nonoverlap=max_truncate_nonoverlap+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SICK/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_SICK_corpus(rootPath+'vocab.txt', rootPath+'train_plus_dev.txt', rootPath+'test.txt', max_truncate,maxSentLength, entailment=True)#vocab_size contain train, dev and test
datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#nonoverlap
indices_train_nonoverlap, trainY_nonoverlap, trainLengths_nonoverlap, normalized_train_length_nonoverlap, trainLeftPad_nonoverlap, trainRightPad_nonoverlap= datasets_nonoverlap[0]
indices_train_l_nonoverlap=indices_train_nonoverlap[::2,:]
indices_train_r_nonoverlap=indices_train_nonoverlap[1::2,:]
trainLengths_l_nonoverlap=trainLengths_nonoverlap[::2]
trainLengths_r_nonoverlap=trainLengths_nonoverlap[1::2]
normalized_train_length_l_nonoverlap=normalized_train_length_nonoverlap[::2]
normalized_train_length_r_nonoverlap=normalized_train_length_nonoverlap[1::2]
trainLeftPad_l_nonoverlap=trainLeftPad_nonoverlap[::2]
trainLeftPad_r_nonoverlap=trainLeftPad_nonoverlap[1::2]
trainRightPad_l_nonoverlap=trainRightPad_nonoverlap[::2]
trainRightPad_r_nonoverlap=trainRightPad_nonoverlap[1::2]
indices_test_nonoverlap, testY_nonoverlap, testLengths_nonoverlap,normalized_test_length_nonoverlap, testLeftPad_nonoverlap, testRightPad_nonoverlap= datasets_nonoverlap[1]
indices_test_l_nonoverlap=indices_test_nonoverlap[::2,:]
indices_test_r_nonoverlap=indices_test_nonoverlap[1::2,:]
testLengths_l_nonoverlap=testLengths_nonoverlap[::2]
testLengths_r_nonoverlap=testLengths_nonoverlap[1::2]
normalized_test_length_l_nonoverlap=normalized_test_length_nonoverlap[::2]
normalized_test_length_r_nonoverlap=normalized_test_length_nonoverlap[1::2]
testLeftPad_l_nonoverlap=testLeftPad_nonoverlap[::2]
testLeftPad_r_nonoverlap=testLeftPad_nonoverlap[1::2]
testRightPad_l_nonoverlap=testRightPad_nonoverlap[::2]
testRightPad_r_nonoverlap=testRightPad_nonoverlap[1::2]
'''
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
'''
indices_train_l_nonoverlap=theano.shared(numpy.asarray(indices_train_l_nonoverlap, dtype=theano.config.floatX), borrow=True)
indices_train_r_nonoverlap=theano.shared(numpy.asarray(indices_train_r_nonoverlap, dtype=theano.config.floatX), borrow=True)
indices_test_l_nonoverlap=theano.shared(numpy.asarray(indices_test_l_nonoverlap, dtype=theano.config.floatX), borrow=True)
indices_test_r_nonoverlap=theano.shared(numpy.asarray(indices_test_r_nonoverlap, dtype=theano.config.floatX), borrow=True)
indices_train_l_nonoverlap=T.cast(indices_train_l_nonoverlap, 'int64')
indices_train_r_nonoverlap=T.cast(indices_train_r_nonoverlap, 'int64')
indices_test_l_nonoverlap=T.cast(indices_test_l_nonoverlap, 'int64')
indices_test_r_nonoverlap=T.cast(indices_test_r_nonoverlap, 'int64')
rand_values_nonoverlap=random_value_normal((vocab_size_nonoverlap+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values_nonoverlap[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values_nonoverlap=load_word2vec_to_init(rand_values_nonoverlap, rootPath+'vocab_nonoverlap_train_plus_dev_in_word2vec_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings_nonoverlap=theano.shared(value=rand_values_nonoverlap, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_l_nonoverlap = T.lmatrix('x_index_l_nonoverlap') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
x_index_r_nonoverlap = T.lmatrix('x_index_r_nonoverlap')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
left_l_nonoverlap=T.lscalar()
right_l_nonoverlap=T.lscalar()
left_r_nonoverlap=T.lscalar()
right_r_nonoverlap=T.lscalar()
length_l_nonoverlap=T.lscalar()
length_r_nonoverlap=T.lscalar()
norm_length_l_nonoverlap=T.dscalar()
norm_length_r_nonoverlap=T.dscalar()
mts=T.dmatrix()
extra=T.dmatrix()
discri=T.dmatrix()
#wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
ishape_nonoverlap = (emb_size, maxSentLength_nonoverlap)
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
#length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_l_input_nonoverlap = embeddings_nonoverlap[x_index_l_nonoverlap.flatten()].reshape((batch_size,maxSentLength_nonoverlap, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input_nonoverlap = embeddings_nonoverlap[x_index_r_nonoverlap.flatten()].reshape((batch_size,maxSentLength_nonoverlap, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer0_l_nonoverlap = Conv_with_input_para(rng, input=layer0_l_input_nonoverlap,
image_shape=(batch_size, 1, ishape_nonoverlap[0], ishape_nonoverlap[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r_nonoverlap = Conv_with_input_para(rng, input=layer0_r_input_nonoverlap,
image_shape=(batch_size, 1, ishape_nonoverlap[0], ishape_nonoverlap[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output_nonoverlap=debug_print(layer0_l_nonoverlap.output, 'layer0_l_nonoverlap.output')
layer0_r_output_nonoverlap=debug_print(layer0_r_nonoverlap.output, 'layer0_r_nonoverlap.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
layer1_nonoverlap=Average_Pooling_for_Top(rng, input_l=layer0_l_output_nonoverlap, input_r=layer0_r_output_nonoverlap, kern=nkerns[0],
left_l=left_l_nonoverlap, right_l=right_l_nonoverlap, left_r=left_r_nonoverlap, right_r=right_r_nonoverlap,
length_l=length_l_nonoverlap+filter_size[1]-1, length_r=length_r_nonoverlap+filter_size[1]-1,
dim=maxSentLength_nonoverlap+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
linear=Linear(norm_uni_l, norm_uni_r)
poly=Poly(norm_uni_l, norm_uni_r)
sigmoid=Sigmoid(norm_uni_l, norm_uni_r)
rbf=RBF(norm_uni_l, norm_uni_r)
gesd=GESD(norm_uni_l, norm_uni_r)
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
sum_uni_l_nonoverlap=T.sum(layer0_l_input_nonoverlap, axis=3).reshape((1, emb_size))
aver_uni_l_nonoverlap=sum_uni_l_nonoverlap/layer0_l_input_nonoverlap.shape[3]
norm_uni_l_nonoverlap=sum_uni_l_nonoverlap/T.sqrt((sum_uni_l_nonoverlap**2).sum())
sum_uni_r_nonoverlap=T.sum(layer0_r_input_nonoverlap, axis=3).reshape((1, emb_size))
aver_uni_r_nonoverlap=sum_uni_r_nonoverlap/layer0_r_input_nonoverlap.shape[3]
norm_uni_r_nonoverlap=sum_uni_r_nonoverlap/T.sqrt((sum_uni_r_nonoverlap**2).sum())
uni_cosine_nonoverlap=cosine(sum_uni_l_nonoverlap, sum_uni_r_nonoverlap)
aver_uni_cosine_nonoverlap=cosine(aver_uni_l_nonoverlap, aver_uni_r_nonoverlap)
uni_sigmoid_simi_nonoverlap=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l_nonoverlap, norm_uni_r_nonoverlap.T)).reshape((1,1)),'uni_sigmoid_simi')
eucli_1_nonoverlap=1.0/(1.0+EUCLID(sum_uni_l_nonoverlap, sum_uni_r_nonoverlap))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l_nonoverlap=norm_length_l_nonoverlap.reshape((1,1))
len_r_nonoverlap=norm_length_r_nonoverlap.reshape((1,1))
'''
len_l_nonoverlap=length_l_nonoverlap.reshape((1,1))
len_r_nonoverlap=length_r_nonoverlap.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([mts,
eucli_1,uni_cosine,#linear, poly,sigmoid,rbf, gesd, #sum_uni_r-sum_uni_l,
eucli_1_nonoverlap,uni_cosine_nonoverlap,
layer1.output_eucli_to_simi,layer1.output_cosine, #layer1.output_vector_r-layer1.output_vector_l,
layer1_nonoverlap.output_eucli_to_simi,layer1_nonoverlap.output_cosine,
len_l, len_r,
len_l_nonoverlap, len_r_nonoverlap,
extra
#discri
#wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=14+(2*2)+(2*2)+(2*2)+9, n_out=3)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg= Diversify_Reg(layer3.W.T)+Diversify_Reg(conv_W_into_matrix)
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg+Div_reg*diversify_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.errors(y),layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
x_index_l_nonoverlap: indices_test_l_nonoverlap[index: index + batch_size],
x_index_r_nonoverlap: indices_test_r_nonoverlap[index: index + batch_size],
left_l_nonoverlap: testLeftPad_l_nonoverlap[index],
right_l_nonoverlap: testRightPad_l_nonoverlap[index],
left_r_nonoverlap: testLeftPad_r_nonoverlap[index],
right_r_nonoverlap: testRightPad_r_nonoverlap[index],
length_l_nonoverlap: testLengths_l_nonoverlap[index],
length_r_nonoverlap: testLengths_r_nonoverlap[index],
norm_length_l_nonoverlap: normalized_test_length_l_nonoverlap[index],
norm_length_r_nonoverlap: normalized_test_length_r_nonoverlap[index],
mts: mt_test[index: index + batch_size],
extra: extra_test[index: index + batch_size],
discri:discri_test[index: index + batch_size]
#wmf: wm_test[index: index + batch_size]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params
params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
# updates = []
# grads = T.grad(cost, params)
# i = theano.shared(numpy.float64(0.))
# i_t = i + 1.
# fix1 = 1. - (1. - b1)**i_t
# fix2 = 1. - (1. - b2)**i_t
# lr_t = lr * (T.sqrt(fix2) / fix1)
# for p, g in zip(params, grads):
# m = theano.shared(p.get_value() * 0.)
# v = theano.shared(p.get_value() * 0.)
# m_t = (b1 * g) + ((1. - b1) * m)
# v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
# g_t = m_t / (T.sqrt(v_t) + e)
# p_t = p - (lr_t * g_t)
# updates.append((m, m_t))
# updates.append((v, v_t))
# updates.append((p, p_t))
# updates.append((i, i_t))
# return updates
#
# updates=Adam(cost=cost, params=params, lr=0.0005)
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
x_index_l_nonoverlap: indices_train_l_nonoverlap[index: index + batch_size],
x_index_r_nonoverlap: indices_train_r_nonoverlap[index: index + batch_size],
left_l_nonoverlap: trainLeftPad_l_nonoverlap[index],
right_l_nonoverlap: trainRightPad_l_nonoverlap[index],
left_r_nonoverlap: trainLeftPad_r_nonoverlap[index],
right_r_nonoverlap: trainRightPad_r_nonoverlap[index],
length_l_nonoverlap: trainLengths_l_nonoverlap[index],
length_r_nonoverlap: trainLengths_r_nonoverlap[index],
norm_length_l_nonoverlap: normalized_train_length_l_nonoverlap[index],
norm_length_r_nonoverlap: normalized_train_length_r_nonoverlap[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
x_index_l_nonoverlap: indices_train_l_nonoverlap[index: index + batch_size],
x_index_r_nonoverlap: indices_train_r_nonoverlap[index: index + batch_size],
left_l_nonoverlap: trainLeftPad_l_nonoverlap[index],
right_l_nonoverlap: trainRightPad_l_nonoverlap[index],
left_r_nonoverlap: trainLeftPad_r_nonoverlap[index],
right_r_nonoverlap: trainRightPad_r_nonoverlap[index],
length_l_nonoverlap: trainLengths_l_nonoverlap[index],
length_r_nonoverlap: trainLengths_r_nonoverlap[index],
norm_length_l_nonoverlap: normalized_train_length_l_nonoverlap[index],
norm_length_r_nonoverlap: normalized_train_length_r_nonoverlap[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
pre_max=-1
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
for i in test_batch_start:
test_loss, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
lr=linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr=lr.predict(test_features)
corr_count=0
corr_lr=0
corr_neu=0
neu_co=0
corr_ent=0
ent_co=0
corr_contr=0
contr_co=0
test_size=len(test_y)
for i in range(test_size):
if results_lr[i]==test_y[i]:
corr_lr+=1
if test_y[i]==0:#NEUTRAL
neu_co+=1
if results[i]==test_y[i]:
corr_neu+=1
elif test_y[i]==1:#ENTAILMENT
ent_co+=1
if results[i]==test_y[i]:
corr_ent+=1
elif test_y[i]==2:#CONTRADICTION
contr_co+=1
if results[i]==test_y[i]:
corr_contr+=1
#if numpy.absolute(results_lr[i]-test_y[i])<0.5:
# corr_lr+=1
corr_count=corr_neu+corr_ent+corr_contr
acc=corr_count*1.0/test_size
acc_neu=corr_neu*1.0/neu_co
acc_ent=corr_ent*1.0/ent_co
acc_contr=corr_contr*1.0/contr_co
acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_epoch=epoch
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
if acc_lr> max_acc:
max_acc=acc_lr
best_epoch=epoch
print '\t\t\tsvm:', acc, 'lr:', acc_lr, 'max:', max_acc,'(at',best_epoch,')','Neu:',acc_neu, 'Ent:',acc_ent, 'Contr:',acc_contr
if max_acc > pre_max:
write_feature_train=open(rootPath+'train_feature_'+str(max_acc)+'.txt', 'w')
write_feature_test=open(rootPath+'test_feature_'+str(max_acc)+'.txt', 'w')
for i in range(len(train_features)):
write_feature_train.write(' '.join(map(str, train_features[i]))+'\n')
for i in range(len(test_features)):
write_feature_test.write(' '.join(map(str, test_features[i]))+'\n')
write_feature_train.close()
write_feature_test.close()
print 'features stored over'
pre_max=max_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, batch_size=500, test_batch_size=500, emb_size=10, hidden_size=10,
L2_weight=0.0001, margin=0.5,
train_size=4000000, test_size=1000,
max_context_len=25, max_span_len=7, max_q_len=40, max_EM=0.0):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = np.random.RandomState(23455)
word2id,train_questions,train_questions_mask,train_lefts,train_lefts_mask,train_spans,train_spans_mask,train_rights,train_rights_mask=load_SQUAD_hinrich(train_size, max_context_len, max_span_len, max_q_len)
test_ground_truth,all_candidates_f1,test_questions,test_questions_mask,test_lefts,test_lefts_mask,test_spans,test_spans_mask,test_rights,test_rights_mask=load_dev_hinrich(word2id, test_size, max_context_len, max_span_len, max_q_len)
overall_vocab_size=len(word2id)
print 'vocab size:', overall_vocab_size
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, np.random.RandomState(1234))
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
left=T.imatrix() #(2*batch, len)
left_mask=T.fmatrix() #(2*batch, len)
span=T.imatrix() #(2*batch, span_len)
span_mask=T.fmatrix() #(2*batch, span_len)
right=T.imatrix() #(2*batch, len)
right_mask=T.fmatrix() #(2*batch, len)
q=T.imatrix() #(2*batch, len_q)
q_mask=T.fmatrix() #(2*batch, len_q)
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
GRU1_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U2, W2, b2=create_GRU_para(rng, hidden_size, hidden_size)
U2_b, W2_b, b2_b=create_GRU_para(rng, hidden_size, hidden_size)
GRU2_para=[U2, W2, b2, U2_b, W2_b, b2_b]
W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
attend_para=[W_a1, W_a2]
params = [embeddings]+GRU1_para+attend_para+GRU2_para
# load_model_from_file(rootPath+'Best_Para_dim'+str(emb_size), params)
left_input = embeddings[left.flatten()].reshape((left.shape[0], left.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
span_input = embeddings[span.flatten()].reshape((span.shape[0], span.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_span)
right_input = embeddings[right.flatten()].reshape((right.shape[0], right.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_context)
q_input = embeddings[q.flatten()].reshape((q.shape[0], q.shape[1], emb_size)).transpose((0, 2,1)) # (2*batch_size, emb_size, len_q)
left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=left_input, Mask=left_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
left_reps=left_model.output_tensor #(batch, emb, para_len)
span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=span_input, Mask=span_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
span_reps=span_model.output_tensor #(batch, emb, para_len)
right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=right_input, Mask=right_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
right_reps=right_model.output_tensor #(batch, emb, para_len)
q_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=q_input, Mask=q_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
q_reps=q_model.output_tensor #(batch, emb, para_len)
#interaction
left_reps_via_q_reps, q_reps_via_left_reps=attention_dot_prod_between_2tensors(left_reps, q_reps)
span_reps_via_q_reps, q_reps_via_span_reps=attention_dot_prod_between_2tensors(span_reps, q_reps)
right_reps_via_q_reps, q_reps_via_right_reps=attention_dot_prod_between_2tensors(right_reps, q_reps)
# q_reps_via_left_reps=attention_dot_prod_between_2tensors(q_reps, left_reps)
# q_reps_via_span_reps=attention_dot_prod_between_2tensors(q_reps, span_reps)
# q_reps_via_right_reps=attention_dot_prod_between_2tensors(q_reps, right_reps)
#combine
origin_W=normalize_matrix(W_a1)
attend_W=normalize_matrix(W_a2)
left_origin_reps=T.dot(left_reps.dimshuffle(0, 2,1), origin_W)
span_origin_reps=T.dot(span_reps.dimshuffle(0, 2,1), origin_W)
right_origin_reps=T.dot(right_reps.dimshuffle(0, 2,1), origin_W)
q_origin_reps=T.dot(q_reps.dimshuffle(0, 2,1), origin_W)
left_attend_q_reps=T.dot(q_reps_via_left_reps.dimshuffle(0, 2,1), attend_W)
span_attend_q_reps=T.dot(q_reps_via_span_reps.dimshuffle(0, 2,1), attend_W)
right_attend_q_reps=T.dot(q_reps_via_right_reps.dimshuffle(0, 2,1), attend_W)
q_attend_left_reps=T.dot(left_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_span_reps=T.dot(span_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
q_attend_right_reps=T.dot(right_reps_via_q_reps.dimshuffle(0, 2,1), attend_W)
add_left=left_origin_reps+q_attend_left_reps #(2*batch, len ,hidden)
add_span=span_origin_reps+q_attend_span_reps
add_right=right_origin_reps+q_attend_right_reps
add_q_by_left=q_origin_reps+left_attend_q_reps
add_q_by_span=q_origin_reps+span_attend_q_reps
add_q_by_right=q_origin_reps+right_attend_q_reps
#second GRU
add_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_left.dimshuffle(0,2,1), Mask=left_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_left_reps=add_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_span.dimshuffle(0,2,1), Mask=span_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_span_reps=add_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_right.dimshuffle(0,2,1), Mask=right_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_right_reps=add_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_left_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_left.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_left_reps=add_q_by_left_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_span_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_span.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_span_reps=add_q_by_span_model.output_sent_rep_maxpooling #(batch, hidden_dim)
add_q_by_right_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=add_q_by_right.dimshuffle(0,2,1), Mask=q_mask, hidden_dim=hidden_size,U=U2,W=W2,b=b2,Ub=U2_b,Wb=W2_b,bb=b2_b)
add_q_by_right_reps=add_q_by_right_model.output_sent_rep_maxpooling #(batch, hidden_dim)
paragraph_concat=T.concatenate([add_left_reps, add_span_reps, add_right_reps], axis=1) #(batch, 3*hidden)
question_concat=T.concatenate([add_q_by_left_reps, add_q_by_span_reps, add_q_by_right_reps], axis=1) #(batch, 3*hidden)
simi_list=cosine_row_wise_twoMatrix(paragraph_concat, question_concat) #(2*batch)
pos_simi_vec=simi_list[::2]
neg_simi_vec=simi_list[1::2]
raw_loss=T.maximum(0.0, margin+neg_simi_vec-pos_simi_vec)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=T.sum(raw_loss)#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([left, left_mask, span, span_mask, right, right_mask, q, q_mask], simi_list, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size #batch_size means how many pairs
remain_train=train_size%batch_size
# train_batch_start=list(np.arange(n_train_batches)*batch_size*2)+[train_size*2-batch_size*2] # always ou shu
if remain_train>0:
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
else:
train_batch_start=list(np.arange(n_train_batches)*batch_size)
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_odd_ids = list(np.arange(train_size)*2)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_odd_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
if iter%100==0:
print 'iter:', iter
iter_accu+=1
train_id_list=[[train_odd_id, train_odd_id+1] for train_odd_id in train_odd_ids[para_id:para_id+batch_size]]
train_id_list=sum(train_id_list,[])
# print train_id_list
cost_i+= train_model(
np.asarray([train_lefts[id] for id in train_id_list], dtype='int32'),
np.asarray([train_lefts_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_spans[id] for id in train_id_list], dtype='int32'),
np.asarray([train_spans_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_rights[id] for id in train_id_list], dtype='int32'),
np.asarray([train_rights_mask[id] for id in train_id_list], dtype=theano.config.floatX),
np.asarray([train_questions[id] for id in train_id_list], dtype='int32'),
np.asarray([train_questions_mask[id] for id in train_id_list], dtype=theano.config.floatX))
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
exact_match=0.0
F1_match=0.0
for test_pair_id in range(test_size):
test_example_lefts=test_lefts[test_pair_id]
test_example_lefts_mask=test_lefts_mask[test_pair_id]
test_example_spans=test_spans[test_pair_id]
test_example_spans_mask=test_spans_mask[test_pair_id]
test_example_rights=test_rights[test_pair_id]
test_example_rights_mask=test_rights_mask[test_pair_id]
test_example_questions=test_questions[test_pair_id]
test_example_questions_mask=test_questions_mask[test_pair_id]
test_example_candidates_f1=all_candidates_f1[test_pair_id]
test_example_size=len(test_example_lefts)
# print 'test_pair_id, test_example_size:', test_pair_id, test_example_size
if test_example_size < test_batch_size:
#pad
pad_size=test_batch_size-test_example_size
test_example_lefts+=test_example_lefts[-1:]*pad_size
test_example_lefts_mask+=test_example_lefts_mask[-1:]*pad_size
test_example_spans+=test_example_spans[-1:]*pad_size
test_example_spans_mask+=test_example_spans_mask[-1:]*pad_size
test_example_rights+=test_example_rights[-1:]*pad_size
test_example_rights_mask+=test_example_rights_mask[-1:]*pad_size
test_example_questions+=test_example_questions[-1:]*pad_size
test_example_questions_mask+=test_example_questions_mask[-1:]*pad_size
test_example_candidates_f1+=test_example_candidates_f1[-1:]*pad_size
test_example_size=test_batch_size
n_test_batches=test_example_size/test_batch_size
n_test_remain=test_example_size%test_batch_size
if n_test_remain > 0:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)+[test_example_size-test_batch_size]
else:
test_batch_start=list(np.arange(n_test_batches)*test_batch_size)
all_simi_list=[]
all_cand_list=[]
for test_para_id in test_batch_start:
simi_return_vector=test_model(
np.asarray(test_example_lefts[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_lefts_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_spans[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_spans_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_rights[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_rights_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
np.asarray(test_example_questions[test_para_id:test_para_id+test_batch_size], dtype='int32'),
np.asarray(test_example_questions_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX))
candidate_f1_list=test_example_candidates_f1[test_para_id:test_para_id+test_batch_size]
all_simi_list+=list(simi_return_vector)
all_cand_list+=candidate_f1_list
top1_f1=all_cand_list[np.argsort(all_simi_list)[-1]]
# print top1_cand, test_ground_truth[test_pair_id]
if top1_f1 == 1.0:
exact_match+=1
# F1=macrof1(top1_cand, test_ground_truth[test_pair_id])
# print '\t\t\t', F1
F1_match+=top1_f1
# match_amount=len(pred_ans_set & q_gold_ans_set)
# # print 'q_gold_ans_set:', q_gold_ans_set
# # print 'pred_ans_set:', pred_ans_set
# if match_amount>0:
# exact_match+=match_amount*1.0/len(pred_ans_set)
F1_acc=F1_match/test_size
exact_acc=exact_match/test_size
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
# store_model_to_file(params, emb_size)
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Para_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.06,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=[4, 4],
maxSentLength=64,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.0006,
update_freq=1,
norm_threshold=5.0,
max_truncate=40):
maxSentLength = max_truncate + 2 * (window_width[0] - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_wikiQA_corpus(
rootPath + 'vocab.txt', rootPath + 'WikiQA-train.txt',
rootPath + 'test_filtered.txt', max_truncate,
maxSentLength) #vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test = load_mts_wikiQA(
mtPath + 'result_train/concate_2mt_train.txt',
mtPath + 'result_test/concate_2mt_test.txt')
wm_train, wm_test = load_wmf_wikiQA(
rootPath + 'train_word_matching_scores.txt',
rootPath + 'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int64')
indices_train_r = T.cast(indices_train_r, 'int64')
indices_test_l = T.cast(indices_test_l, 'int64')
indices_test_r = T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l = T.lscalar()
right_l = T.lscalar()
left_r = T.lscalar()
right_r = T.lscalar()
length_l = T.lscalar()
length_r = T.lscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
wmf = T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width[0])
filter_size_2 = (nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_size[0],
filter_size[1]))
load_model_from_file([conv_W, conv_b])
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng,
input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_r = Conv_with_input_para(rng,
input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output')
layer0_para = [conv_W, conv_b]
layer1 = Average_Pooling(rng,
input_l=layer0_l_output,
input_r=layer0_r_output,
kern=nkerns[0],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1,
window_size=window_width[0],
maxSentLength=maxSentLength)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1,
filter_size_2[0],
filter_size_2[1]))
#load_model_from_file([conv2_W, conv2_b])
layer2_l = Conv_with_input_para(
rng,
input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_r = Conv_with_input_para(
rng,
input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_para = [conv2_W, conv2_b]
layer3 = Average_Pooling_for_Top(rng,
input_l=layer2_l.output,
input_r=layer2_r.output,
kern=nkerns[1],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size_2[1] - 1,
length_r=length_r + filter_size_2[1] - 1,
dim=maxSentLength + filter_size_2[1] - 1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l = sum_uni_l / layer0_l_input.shape[3]
norm_uni_l = sum_uni_l / T.sqrt((sum_uni_l**2).sum())
sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r = sum_uni_r / layer0_r_input.shape[3]
norm_uni_r = sum_uni_r / T.sqrt((sum_uni_r**2).sum())
uni_cosine = cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine = cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi = debug_print(
T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1, 1)),
'uni_sigmoid_simi')
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input = T.concatenate(
[ #mts,
uni_cosine, #eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.
output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
layer3.output_cosine,
len_l,
len_r,
wmf
],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3 = LogisticRegression(rng,
input=layer3_input,
n_in=(1) + (1) + (1) + 2 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer3.W**2).sum() + (conv2_W**2).sum(), 'L2_reg'
) #+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer3.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index], [layer3.prop_for_posi, layer3_input, y],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size],
wmf: wm_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params + layer2_para #+layer0_para
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches / 5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
svm_max = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs = []
test_y = []
test_features = []
for i in test_batch_start:
prob_i, layer3_input, y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR = compute_map_mrr(rootPath + 'test_filtered.txt',
test_probs)
#now, check MAP and MRR
print(
('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches, MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm = clf.decision_function(test_features)
MAP_svm, MRR_svm = compute_map_mrr(
rootPath + 'test_filtered.txt', results_svm)
lr = LinearRegression().fit(train_features, train_y)
results_lr = lr.predict(test_features)
MAP_lr, MRR_lr = compute_map_mrr(
rootPath + 'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
#after each epoch, increase the batch_size
if epoch % 2 == 1:
update_freq = update_freq * 1
else:
update_freq = update_freq / 1
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.01, n_epochs=2000, batch_size=100, emb_size=300, char_emb_size=20, hidden_size=300,
L2_weight=0.0001, p_len_limit=400, test_p_len_limit=100, q_len_limit=20, char_len=15, filter_size = [5,5],
char_filter_size=4, margin=0.5, max_EM=50.302743615):
test_batch_size=batch_size*10
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
word2id={}
char2id={}
#questions,paragraphs,q_masks,p_masks,labels, word2id
train_Q_list,train_para_list, train_Q_mask, train_para_mask, train_Q_char_list,train_para_char_list, train_Q_char_mask, train_para_char_mask, train_label_list, word2id, char2id=load_squad_cnn_rank_word_train(word2id, char2id, p_len_limit, q_len_limit, char_len)
train_size=len(train_para_list)
test_Q_list, test_para_list, test_Q_mask, test_para_mask,test_Q_char_list, test_para_char_list, test_Q_char_mask, test_para_char_mask, test_label_list, word2id, char2id, test_para_wordlist_list= load_squad_cnn_rank_word_dev(word2id, char2id, test_p_len_limit, q_len_limit, char_len)
test_size=len(test_para_list)
train_Q_list = numpy.asarray(train_Q_list, dtype='int32')
train_para_list = numpy.asarray(train_para_list, dtype='int32')
train_Q_mask = numpy.asarray(train_Q_mask, dtype=theano.config.floatX)
train_para_mask = numpy.asarray(train_para_mask, dtype=theano.config.floatX)
train_Q_char_list = numpy.asarray(train_Q_char_list, dtype='int32')
train_para_char_list = numpy.asarray(train_para_char_list, dtype='int32')
train_Q_char_mask = numpy.asarray(train_Q_char_mask, dtype=theano.config.floatX)
train_para_char_mask = numpy.asarray(train_para_char_mask, dtype=theano.config.floatX)
train_label_list = numpy.asarray(train_label_list, dtype='int32')
test_Q_list = numpy.asarray(test_Q_list, dtype='int32')
test_para_list = numpy.asarray(test_para_list, dtype='int32')
test_Q_mask = numpy.asarray(test_Q_mask, dtype=theano.config.floatX)
test_para_mask = numpy.asarray(test_para_mask, dtype=theano.config.floatX)
test_Q_char_list = numpy.asarray(test_Q_char_list, dtype='int32')
test_para_char_list = numpy.asarray(test_para_char_list, dtype='int32')
test_Q_char_mask = numpy.asarray(test_Q_char_mask, dtype=theano.config.floatX)
test_para_char_mask = numpy.asarray(test_para_char_mask, dtype=theano.config.floatX)
vocab_size = len(word2id)
print 'vocab size: ', vocab_size
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, rng)
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_glove()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
char_size = len(char2id)
print 'char size: ', char_size
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, rng)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
gold_indices= T.ivector() #batch, one gold word for each sample
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
char_paragraph = T.imatrix() #(batch, char_len*p_len)
char_questions = T.imatrix()
char_para_mask=T.fmatrix()
char_q_mask=T.fmatrix()
# true_p_len = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size = paragraph.shape[0]
true_p_len = paragraph.shape[1]
common_input_p=embeddings[paragraph.flatten()].reshape((true_batch_size,true_p_len, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_q=embeddings[questions.flatten()].reshape((true_batch_size,q_len_limit, emb_size))
char_common_input_p=char_embeddings[char_paragraph.flatten()].reshape((true_batch_size*true_p_len, char_len, char_emb_size)) #the input format can be adapted into CNN or GRU or LSTM
char_common_input_q=char_embeddings[char_questions.flatten()].reshape((true_batch_size*q_len_limit, char_len, char_emb_size))
char_p_masks = char_para_mask.reshape((true_batch_size*true_p_len, char_len))
char_q_masks = char_q_mask.reshape((true_batch_size*q_len_limit, char_len))
char_LSTM_para_dict=create_LSTM_para(rng, char_emb_size, char_emb_size)
char_LSTM_para_dict_bw=create_LSTM_para(rng, char_emb_size, char_emb_size)
char_lstm_layer_p=Bd_LSTM_Batch_Tensor_Input_with_Mask(char_common_input_p.dimshuffle(0,2,1), char_p_masks, char_emb_size, char_LSTM_para_dict,char_LSTM_para_dict_bw)
char_word_embeddings_p = char_lstm_layer_p.output_sent_rep_conc.reshape((true_batch_size, true_p_len, 2*char_emb_size)).dimshuffle(0, 2,1) #(batch, 2*hidden)
char_lstm_layer_q=Bd_LSTM_Batch_Tensor_Input_with_Mask(char_common_input_q.dimshuffle(0,2,1), char_q_masks, char_emb_size, char_LSTM_para_dict,char_LSTM_para_dict_bw)
char_word_embeddings_q = char_lstm_layer_q.output_sent_rep_conc.reshape((true_batch_size, q_len_limit, 2*char_emb_size)).dimshuffle(0, 2,1) #(batch, 2*hidden)
LSTM_para_dict=create_LSTM_para(rng, 2*char_emb_size+emb_size,hidden_size) #40+300
LSTM_para_dict_bw=create_LSTM_para(rng, 2*char_emb_size+emb_size,hidden_size)
p_input2lstm = T.concatenate([common_input_p.dimshuffle(0,2,1), char_word_embeddings_p], axis=1) #(batch, emb_size+char_emb_size, p_len)
q_input2lstm = T.concatenate([common_input_q.dimshuffle(0,2,1), char_word_embeddings_q], axis=1) #(batch, emb_size+char_emb_size, p_len)
lstm_layer_p=Bd_LSTM_Batch_Tensor_Input_with_Mask(p_input2lstm, para_mask, hidden_size, LSTM_para_dict,LSTM_para_dict_bw)
p_tensor3 = lstm_layer_p.output_tensor_conc #(batch, 2*hidden, p_len)
lstm_layer_q=Bd_LSTM_Batch_Tensor_Input_with_Mask(q_input2lstm, q_mask, hidden_size, LSTM_para_dict,LSTM_para_dict_bw)
q_reps = lstm_layer_q.output_sent_rep_conc #(batch, 2*hidden)
NN_para=char_LSTM_para_dict.values()+char_LSTM_para_dict_bw.values()+LSTM_para_dict.values()+LSTM_para_dict_bw.values()
input4score = T.concatenate([p_tensor3, T.repeat(q_reps.dimshuffle(0,1,'x'), true_p_len, axis=2)], axis=1) #(batch, 4*hidden, p_len)
HL_1_para = create_ensemble_para(rng, hidden_size, 4*hidden_size)
HL_2_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_3_para = create_ensemble_para(rng, hidden_size, hidden_size)
HL_4_para = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, hidden_size)
norm_U_a=normalize_matrix(U_a)
norm_HL_1_para=normalize_matrix(HL_1_para)
norm_HL_2_para=normalize_matrix(HL_2_para)
norm_HL_3_para=normalize_matrix(HL_3_para)
norm_HL_4_para=normalize_matrix(HL_4_para)
span_scores_matrix = add_HLs_2_tensor3(input4score, norm_HL_1_para,norm_HL_2_para,norm_HL_3_para,norm_HL_4_para, norm_U_a, true_batch_size,true_p_len)
span_scores=T.nnet.softmax(span_scores_matrix) #(batch, para_len)
loss_neg_likelihood=-T.mean(T.log(span_scores[T.arange(true_batch_size), gold_indices]))
#ranking loss
tanh_span_scores_matrix = span_scores#T.tanh(span_scores_matrix) #(batch, gram_size)
index_matrix = T.zeros((true_batch_size, p_len_limit), dtype=theano.config.floatX)
new_index_matrix = T.set_subtensor(index_matrix[T.arange(true_batch_size), gold_indices], 1.0)
prob_batch_posi = tanh_span_scores_matrix[new_index_matrix.nonzero()]
prob_batch_nega = tanh_span_scores_matrix[(1.0-new_index_matrix).nonzero()]
repeat_posi = T.extra_ops.repeat(prob_batch_posi, prob_batch_nega.shape[0], axis=0)
repeat_nega = T.extra_ops.repeat(prob_batch_nega.dimshuffle('x',0), prob_batch_posi.shape[0], axis=0).flatten()
loss_rank = T.mean(T.maximum(0.0, margin-repeat_posi+repeat_nega))
loss = loss_neg_likelihood + loss_rank
#test
mask_test_return=T.argmax(span_scores_matrix*para_mask, axis=1) #batch
params = [embeddings,char_embeddings]+NN_para+[U_a,HL_1_para,HL_2_para,HL_3_para,HL_4_para]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
#L2_reg = L2norm_paraList(params)
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, char_paragraph, #(batch, char_len*p_len)
char_questions, char_para_mask, char_q_mask], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask,
char_paragraph,
char_questions,
char_para_mask,
char_q_mask], mask_test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_ids[para_id:para_id+batch_size]
cost_i+= train_model(
train_para_list[train_id_batch],
train_Q_list[train_id_batch],
train_label_list[train_id_batch],
train_para_mask[train_id_batch],
train_Q_mask[train_id_batch],
train_para_char_list[train_id_batch],
train_Q_char_list[train_id_batch],
train_para_char_mask[train_id_batch],
train_Q_char_mask[train_id_batch])
#print iter
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# pred_dict={}
q_amount=0
p1=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
test_para_list[test_para_id:test_para_id+test_batch_size],
test_Q_list[test_para_id:test_para_id+test_batch_size],
test_para_mask[test_para_id:test_para_id+test_batch_size],
test_Q_mask[test_para_id:test_para_id+test_batch_size],
test_para_char_list[test_para_id:test_para_id+test_batch_size],
test_Q_char_list[test_para_id:test_para_id+test_batch_size],
test_para_char_mask[test_para_id:test_para_id+test_batch_size],
test_Q_char_mask[test_para_id:test_para_id+test_batch_size])
# test_para_wordlist_batch=test_para_wordlist_list[test_para_id:test_para_id+test_batch_size]
test_label_batch=test_label_list[test_para_id:test_para_id+test_batch_size]
q_amount+=test_batch_size
for q in range(test_batch_size): #for each question
predict_id = batch_predict_ids[q]
ground_ids=test_label_batch[q]
if predict_id in set(ground_ids):
p1+=1
# print batch_predict_ids[q], mask_batch_predict_ids[q], test_p_len_limit - numpy.sum(test_para_mask[test_para_id+q]), scores_i[q], test_para_mask[test_para_id+q]
exact_acc = p1*100.0/q_amount
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
# if max_exact_acc > max_EM:
# store_model_to_file(rootPath+'Best_Paras_google_'+str(max_exact_acc), params)
# print 'Finished storing best params at:', max_exact_acc
print '\t\tcurrent exact:', exact_acc, '\t\t\tmax exact_acc:', max_exact_acc
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.0001, n_epochs=2000, nkerns=[256,256], batch_size=1, window_width=[4,4],
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0006, Div_reg=0.06, update_freq=1, norm_threshold=5.0, max_truncate=40):
maxSentLength=max_truncate+2*(window_width[0]-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width[0])
filter_size_2=(nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((maxSentLength, emb_size)).transpose()
layer0_r_input = embeddings[x_index_r.flatten()].reshape((maxSentLength, emb_size)).transpose()
l_input_tensor=debug_print(Matrix_Bit_Shift(layer0_l_input[:,left_l:-right_l]), 'l_input_tensor')
r_input_tensor=debug_print(Matrix_Bit_Shift(layer0_r_input[:,left_r:-right_r]), 'r_input_tensor')
addition_l=T.sum(layer0_l_input[:,left_l:-right_l], axis=1)
addition_r=T.sum(layer0_r_input[:,left_r:-right_r], axis=1)
cosine_addition=cosine(addition_l, addition_r)
eucli_addition=1.0/(1.0+EUCLID(addition_l, addition_r))#25.2%
U, W, b=create_GRU_para(rng, emb_size, nkerns[0])
layer0_para=[U, W, b]
layer0_A1 = GRU_Batch_Tensor_Input(X=l_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
layer0_A2 = GRU_Batch_Tensor_Input(X=r_input_tensor, hidden_dim=nkerns[0],U=U,W=W,b=b,bptt_truncate=-1)
cosine_sent=cosine(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep)
eucli_sent=1.0/(1.0+EUCLID(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep))#25.2%
#ibm attentive pooling at extended sentence level
attention_matrix=compute_simi_feature_matrix_with_matrix(layer0_A1.output_matrix, layer0_A2.output_matrix, layer0_A1.dim, layer0_A2.dim, maxSentLength*(maxSentLength+1)/2)
# attention_vec_l_extended=T.nnet.softmax(T.max(attention_matrix, axis=1)).transpose()
# ibm_l_extended=layer0_A1.output_matrix.dot(attention_vec_l_extended).transpose()
# attention_vec_r_extended=T.nnet.softmax(T.max(attention_matrix, axis=0)).transpose()
# ibm_r_extended=layer0_A2.output_matrix.dot(attention_vec_r_extended).transpose()
# cosine_ibm_extended=cosine(ibm_l_extended, ibm_r_extended)
# eucli_ibm_extended=1.0/(1.0+EUCLID(ibm_l_extended, ibm_r_extended))#25.2%
#ibm attentive pooling at original sentence level
simi_matrix_sent=compute_simi_feature_matrix_with_matrix(layer0_A1.output_sent_hiddenstates, layer0_A2.output_sent_hiddenstates, length_l, length_r, maxSentLength)
attention_vec_l=T.nnet.softmax(T.max(simi_matrix_sent, axis=1)).transpose()
ibm_l=layer0_A1.output_sent_hiddenstates.dot(attention_vec_l).transpose()
attention_vec_r=T.nnet.softmax(T.max(simi_matrix_sent, axis=0)).transpose()
ibm_r=layer0_A2.output_sent_hiddenstates.dot(attention_vec_r).transpose()
cosine_ibm=cosine(ibm_l, ibm_r)
eucli_ibm=1.0/(1.0+EUCLID(ibm_l, ibm_r))#25.2%
l_max_attention=T.max(attention_matrix, axis=1)
neighborsArgSorted = T.argsort(l_max_attention)
kNeighborsArg = neighborsArgSorted[-3:]#only average the max 3 vectors
ll = T.sort(kNeighborsArg).flatten() # make y indices in acending lie
r_max_attention=T.max(attention_matrix, axis=0)
neighborsArgSorted_r = T.argsort(r_max_attention)
kNeighborsArg_r = neighborsArgSorted_r[-3:]#only average the max 3 vectors
rr = T.sort(kNeighborsArg_r).flatten() # make y indices in acending lie
l_max_min_attention=debug_print(layer0_A1.output_matrix[:,ll], 'l_max_min_attention')
r_max_min_attention=debug_print(layer0_A2.output_matrix[:,rr], 'r_max_min_attention')
U1, W1, b1=create_GRU_para(rng, nkerns[0], nkerns[1])
layer1_para=[U1, W1, b1]
layer1_A1=GRU_Matrix_Input(X=l_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
layer1_A2=GRU_Matrix_Input(X=r_max_min_attention, word_dim=nkerns[0], hidden_dim=nkerns[1],U=U1,W=W1,b=b1,bptt_truncate=-1)
vec_l=debug_print(layer1_A1.output_vector_last.reshape((1, nkerns[1])), 'vec_l')
vec_r=debug_print(layer1_A2.output_vector_last.reshape((1, nkerns[1])), 'vec_r')
# sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
# aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
# norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
# sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
# aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
# norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
#
uni_cosine=cosine(vec_l, vec_r)
# aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
# uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
# '''
# linear=Linear(sum_uni_l, sum_uni_r)
# poly=Poly(sum_uni_l, sum_uni_r)
# sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
# rbf=RBF(sum_uni_l, sum_uni_r)
# gesd=GESD(sum_uni_l, sum_uni_r)
# '''
eucli_1=1.0/(1.0+EUCLID(vec_l, vec_r))#25.2%
# #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
#
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
#
# '''
# len_l=length_l.reshape((1,1))
# len_r=length_r.reshape((1,1))
# '''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([vec_l, vec_r,
uni_cosine,eucli_1,
cosine_addition, eucli_addition,
# cosine_sent, eucli_sent,
ibm_l.reshape((1, nkerns[0])), ibm_r.reshape((1, nkerns[0])), #2*nkerns[0]+
cosine_ibm, eucli_ibm,
len_l, len_r,wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=(2*nkerns[1]+2)+2 +(2*nkerns[0]+2)+2+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(U** 2).sum()+(W** 2).sum()+(U1** 2).sum()+(W1** 2).sum(), 'L2_reg')#+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
diversify_reg= Diversify_Reg(layer3.W.T)+Diversify_Reg(U[0])+Diversify_Reg(W[0])+Diversify_Reg(U1[0])+Diversify_Reg(W1[0])+Diversify_Reg(U[1])+Diversify_Reg(W[1])+Diversify_Reg(U1[1])+Diversify_Reg(W1[1])+Diversify_Reg(U[2])+Diversify_Reg(W[2])+Diversify_Reg(U1[2])+Diversify_Reg(W1[2])
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg+Div_reg*diversify_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.prop_for_posi,layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ layer1_para+layer0_para#+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
# accumulator=[]
# for para_i in params:
# eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
# accumulator.append(theano.shared(eps_p, borrow=True))
#
# # create a list of gradients for all model parameters
# grads = T.grad(cost, params)
#
# updates = []
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# grad_i=debug_print(grad_i,'grad_i')
# acc = acc_i + T.sqr(grad_i)
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
updates = []
grads = T.grad(cost, params)
i = theano.shared(numpy.float64(0.))
i_t = i + 1.
fix1 = 1. - (1. - b1)**i_t
fix2 = 1. - (1. - b2)**i_t
lr_t = lr * (T.sqrt(fix2) / fix1)
for p, g in zip(params, grads):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (T.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
return updates
updates=Adam(cost=cost, params=params, lr=learning_rate)
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
# print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs=[]
test_y=[]
test_features=[]
for i in test_batch_start:
prob_i, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR=compute_map_mrr(rootPath+'test_filtered.txt', test_probs)
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches,MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm=clf.decision_function(test_features)
MAP_svm, MRR_svm=compute_map_mrr(rootPath+'test_filtered.txt', results_svm)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
MAP_lr, MRR_lr=compute_map_mrr(rootPath+'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, nkerns=[50], batch_size=1, window_width=4,
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0003, update_freq=1, norm_threshold=5.0, max_truncate=40):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([#mts,
uni_cosine,#eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
len_l, len_r,wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=(1)+(1)+2+2, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.prop_for_posi,layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params
params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
svm_max=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs=[]
test_y=[]
test_features=[]
for i in test_batch_start:
prob_i, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR=compute_map_mrr(rootPath+'test_filtered.txt', test_probs)
#now, check MAP and MRR
print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches,MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm=clf.decision_function(test_features)
MAP_svm, MRR_svm=compute_map_mrr(rootPath+'test_filtered.txt', results_svm)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
MAP_lr, MRR_lr=compute_map_mrr(rootPath+'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
#after each epoch, increase the batch_size
if epoch%2==1:
update_freq=update_freq*1
else:
update_freq=update_freq/1
#store the paras after epoch 15
if epoch ==15:
store_model_to_file(params_conv)
print 'Finished storing best conv params'
exit(0)
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.0001, n_epochs=2000, batch_size=20, test_batch_size=200, emb_size=300, hidden_size=300,
L2_weight=0.0001, para_len_limit=400, q_len_limit=40, max_EM=50.302743615):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SQuAD/';
rng = numpy.random.RandomState(23455)
# glove_vocab=set(word2vec.keys())
train_para_list, train_Q_list, train_label_list, train_para_mask, train_mask, word2id, train_feature_matrixlist=load_train(para_len_limit, q_len_limit)
train_size=len(train_para_list)
if train_size!=len(train_Q_list) or train_size!=len(train_label_list) or train_size!=len(train_para_mask):
print 'train_size!=len(Q_list) or train_size!=len(label_list) or train_size!=len(para_mask)'
exit(0)
test_para_list, test_Q_list, test_Q_list_word, test_para_mask, test_mask, overall_vocab_size, overall_word2id, test_text_list, q_ansSet_list, test_feature_matrixlist, q_idlist= load_dev_or_test(word2id, para_len_limit, q_len_limit)
test_size=len(test_para_list)
if test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask):
print 'test_size!=len(test_Q_list) or test_size!=len(test_mask) or test_size!=len(test_para_mask)'
exit(0)
rand_values=random_value_normal((overall_vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in overall_word2id.iteritems()}
# word2vec=load_glove()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.lscalar()
paragraph = T.imatrix('paragraph')
questions = T.imatrix('questions')
# labels = T.imatrix('labels') #(batch, para_len)
gold_indices= T.ivector() #batch
para_mask=T.fmatrix('para_mask')
q_mask=T.fmatrix('q_mask')
extraF=T.ftensor3('extraF') # should be in shape (batch, wordsize, 3)
is_train = T.iscalar()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
true_batch_size=paragraph.shape[0]
norm_extraF=normalize_matrix(extraF)
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
U1_b, W1_b, b1_b=create_GRU_para(rng, emb_size, hidden_size)
paragraph_para=[U1, W1, b1, U1_b, W1_b, b1_b]
U_e1, W_e1, b_e1=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
U_e1_b, W_e1_b, b_e1_b=create_GRU_para(rng, 3*hidden_size+3, hidden_size)
paragraph_para_e1=[U_e1, W_e1, b_e1, U_e1_b, W_e1_b, b_e1_b]
UQ, WQ, bQ=create_GRU_para(rng, emb_size, hidden_size)
UQ_b, WQ_b, bQ_b=create_GRU_para(rng, emb_size, hidden_size)
Q_para=[UQ, WQ, bQ, UQ_b, WQ_b, bQ_b]
# W_a1 = create_ensemble_para(rng, hidden_size, hidden_size)# init_weights((2*hidden_size, hidden_size))
# W_a2 = create_ensemble_para(rng, hidden_size, hidden_size)
U_a = create_ensemble_para(rng, 1, 2*hidden_size) # 3 extra features
# LR_b = theano.shared(value=numpy.zeros((2,),
# dtype=theano.config.floatX), # @UndefinedVariable
# name='LR_b', borrow=True)
HL_paras=[U_a]
params = [embeddings]+paragraph_para+Q_para+paragraph_para_e1+HL_paras
load_model_from_file(rootPath+'Best_Paras_conv_50.302743614', params)
paragraph_input = embeddings[paragraph.flatten()].reshape((true_batch_size, paragraph.shape[1], emb_size)).transpose((0, 2,1)) # (batch_size, emb_size, maxparalen)
concate_paragraph_input=T.concatenate([paragraph_input, norm_extraF.dimshuffle((0,2,1))], axis=1)
paragraph_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=paragraph_input, Mask=para_mask, hidden_dim=hidden_size,U=U1,W=W1,b=b1,Ub=U1_b,Wb=W1_b,bb=b1_b)
para_reps=paragraph_model.output_tensor #(batch, emb, para_len)
# #LSTM
# fwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
# paragraph_para=fwd_LSTM_para_dict.values()+ bwd_LSTM_para_dict.values()# .values returns a list of parameters
# paragraph_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(paragraph_input, para_mask, hidden_size, fwd_LSTM_para_dict, bwd_LSTM_para_dict)
# para_reps=paragraph_model.output_tensor
Qs_emb = embeddings[questions.flatten()].reshape((true_batch_size, questions.shape[1], emb_size)).transpose((0, 2,1)) #(#questions, emb_size, maxsenlength)
questions_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=Qs_emb, Mask=q_mask, hidden_dim=hidden_size, U=UQ,W=WQ,b=bQ, Ub=UQ_b, Wb=WQ_b, bb=bQ_b)
questions_reps_tensor=questions_model.output_tensor
questions_reps=questions_model.output_sent_rep_maxpooling.reshape((true_batch_size, 1, hidden_size)) #(batch, 1, hidden)
questions_reps=T.repeat(questions_reps, para_reps.shape[2], axis=1) #(batch, para_len, hidden)
# #LSTM for questions
# fwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# bwd_LSTM_q_dict=create_LSTM_para(rng, emb_size, hidden_size)
# Q_para=fwd_LSTM_q_dict.values()+ bwd_LSTM_q_dict.values()# .values returns a list of parameters
# questions_model=Bd_LSTM_Batch_Tensor_Input_with_Mask(Qs_emb, q_mask, hidden_size, fwd_LSTM_q_dict, bwd_LSTM_q_dict)
# questions_reps_tensor=questions_model.output_tensor
#
def example_in_batch(para_matrix, q_matrix):
#assume both are (hidden, len)
transpose_para_matrix=para_matrix.T
interaction_matrix=T.dot(transpose_para_matrix, q_matrix) #(para_len, q_len)
norm_interaction_matrix=T.nnet.softmax(interaction_matrix)
# norm_interaction_matrix=T.maximum(0.0, interaction_matrix)
return T.dot(q_matrix, norm_interaction_matrix.T)/T.sum(norm_interaction_matrix.T, axis=0).dimshuffle('x',0) #(len, para_len)
batch_q_reps, updates = theano.scan(fn=example_in_batch,
outputs_info=None,
sequences=[para_reps, questions_reps_tensor]) #batch_q_reps (batch, hidden, para_len)
#para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), all are in (batch, hidden , para_len)
ensemble_para_reps_tensor=T.concatenate([para_reps, batch_q_reps, questions_reps.dimshuffle(0,2,1), norm_extraF.dimshuffle(0,2,1)], axis=1) #(batch, 3*hidden+3, para_len)
para_ensemble_model=Bd_GRU_Batch_Tensor_Input_with_Mask(X=ensemble_para_reps_tensor, Mask=para_mask, hidden_dim=hidden_size,U=U_e1,W=W_e1,b=b_e1,Ub=U_e1_b,Wb=W_e1_b,bb=b_e1_b)
para_reps_tensor4score=para_ensemble_model.output_tensor #(batch, hidden ,para_len)
para_reps_tensor4score = dropout_standard(is_train, para_reps_tensor4score, 0.2, rng)
#for span reps
span_1=T.concatenate([para_reps_tensor4score, para_reps_tensor4score], axis=1) #(batch, 2*hidden ,para_len)
span_2=T.concatenate([para_reps_tensor4score[:,:,:-1], para_reps_tensor4score[:,:,1:]], axis=1) #(batch, 2*hidden ,para_len-1)
span_3=T.concatenate([para_reps_tensor4score[:,:,:-2], para_reps_tensor4score[:,:,2:]], axis=1) #(batch, 2*hidden ,para_len-2)
span_4=T.concatenate([para_reps_tensor4score[:,:,:-3], para_reps_tensor4score[:,:,3:]], axis=1) #(batch, 2*hidden ,para_len-3)
span_5=T.concatenate([para_reps_tensor4score[:,:,:-4], para_reps_tensor4score[:,:,4:]], axis=1) #(batch, 2*hidden ,para_len-4)
span_6=T.concatenate([para_reps_tensor4score[:,:,:-5], para_reps_tensor4score[:,:,5:]], axis=1) #(batch, 2*hidden ,para_len-5)
span_7=T.concatenate([para_reps_tensor4score[:,:,:-6], para_reps_tensor4score[:,:,6:]], axis=1) #(batch, 2*hidden ,para_len-6)
span_8=T.concatenate([para_reps_tensor4score[:,:,:-7], para_reps_tensor4score[:,:,7:]], axis=1) #(batch, 2*hidden ,para_len-7)
span_9=T.concatenate([para_reps_tensor4score[:,:,:-8], para_reps_tensor4score[:,:,8:]], axis=1) #(batch, 2*hidden ,para_len-8)
span_10=T.concatenate([para_reps_tensor4score[:,:,:-9], para_reps_tensor4score[:,:,9:]], axis=1) #(batch, 2*hidden ,para_len-9)
span_11=T.concatenate([para_reps_tensor4score[:,:,:-10], para_reps_tensor4score[:,:,10:]], axis=1) #(batch, 2*hidden ,para_len-10)
span_12=T.concatenate([para_reps_tensor4score[:,:,:-11], para_reps_tensor4score[:,:,11:]], axis=1) #(batch, 2*hidden ,para_len-11)
span_13=T.concatenate([para_reps_tensor4score[:,:,:-12], para_reps_tensor4score[:,:,12:]], axis=1) #(batch, 2*hidden ,para_len-12)
span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7,
span_8, span_9, span_10, span_11, span_12, span_13], axis=2) #(batch, 2*hidden, 13*para_len-78)
test_span_reps=T.concatenate([span_1, span_2, span_3, span_4, span_5, span_6, span_7], axis=2) #(batch, 2*hidden, 5*para_len-10) #, span_6, span_7
#score each span reps
norm_U_a=normalize_matrix(U_a)
span_scores_tensor=T.dot(span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 13*para_len-78, 1)
span_scores=T.nnet.softmax(span_scores_tensor.reshape((true_batch_size, 13*paragraph.shape[1]-78))) #(batch, 7*para_len-21)
loss=-T.sum(T.log(span_scores[T.arange(true_batch_size), gold_indices]))
test_span_scores_tensor=T.dot(test_span_reps.dimshuffle(0,2,1), norm_U_a) #(batch, 7*para_len-21, 1)
test_span_scores=T.nnet.softmax(test_span_scores_tensor.reshape((true_batch_size, 7*paragraph.shape[1]-21))) #(batch, 7*para_len-21)
test_return=T.argmax(test_span_scores, axis=1) #batch
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
# L2_reg =L2norm_paraList([embeddings,U1, W1, U1_b, W1_b,UQ, WQ , UQ_b, WQ_b, W_a1, W_a2, U_a])
# L2_reg = L2norm_paraList([embeddings])
cost=loss#+ConvGRU_1.error#
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# print grad_i.type
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #AdaGrad
updates.append((acc_i, acc))
# updates=Adam(cost, params, lr=0.0001)
train_model = theano.function([paragraph, questions,gold_indices, para_mask, q_mask, extraF, is_train], cost, updates=updates,on_unused_input='ignore')
test_model = theano.function([paragraph, questions,para_mask, q_mask, extraF, is_train], test_return, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
#para_list, Q_list, label_list, mask, vocab_size=load_train()
n_train_batches=train_size/batch_size
# remain_train=train_size%batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/test_batch_size
# remain_test=test_size%batch_size
test_batch_start=list(numpy.arange(n_test_batches)*test_batch_size)+[test_size-test_batch_size]
max_F1_acc=0.0
max_exact_acc=0.0
cost_i=0.0
train_ids = range(train_size)
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
random.shuffle(train_ids)
iter_accu=0
for para_id in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
# haha=para_mask[para_id:para_id+batch_size]
# print haha
# for i in range(batch_size):
# print len(haha[i])
cost_i+= train_model(
numpy.asarray([train_para_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_Q_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_label_list[id] for id in train_ids[para_id:para_id+batch_size]], dtype='int32'),
numpy.asarray([train_para_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_mask[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
numpy.asarray([train_feature_matrixlist[id] for id in train_ids[para_id:para_id+batch_size]], dtype=theano.config.floatX),
1)
#print iter
if iter%10==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
print 'Testing...'
past_time = time.time()
# writefile=codecs.open(rootPath+'predictions.txt', 'w', 'utf-8')
# writefile.write('{')
pred_dict={}
# exact_match=0.0
# F1_match=0.0
q_amount=0
for test_para_id in test_batch_start:
batch_predict_ids=test_model(
numpy.asarray(test_para_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_Q_list[test_para_id:test_para_id+test_batch_size], dtype='int32'),
numpy.asarray(test_para_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_mask[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
numpy.asarray(test_feature_matrixlist[test_para_id:test_para_id+test_batch_size], dtype=theano.config.floatX),
0)
# print distribution_matrix
test_para_wordlist_list=test_text_list[test_para_id:test_para_id+test_batch_size]
# para_gold_ansset_list=q_ansSet_list[test_para_id:test_para_id+test_batch_size]
q_ids_batch=q_idlist[test_para_id:test_para_id+test_batch_size]
# print 'q_ids_batch:', q_ids_batch
# paralist_extra_features=test_feature_matrixlist[test_para_id:test_para_id+batch_size]
# sub_para_mask=test_para_mask[test_para_id:test_para_id+batch_size]
# para_len=len(test_para_wordlist_list[0])
# if para_len!=len(distribution_matrix[0]):
# print 'para_len!=len(distribution_matrix[0]):', para_len, len(distribution_matrix[0])
# exit(0)
# q_size=len(distribution_matrix)
q_amount+=test_batch_size
# print q_size
# print test_para_word_list
# Q_list_inword=test_Q_list_word[test_para_id:test_para_id+test_batch_size]
for q in range(test_batch_size): #for each question
# if len(distribution_matrix[q])!=len(test_label_matrix[q]):
# print 'len(distribution_matrix[q])!=len(test_label_matrix[q]):', len(distribution_matrix[q]), len(test_label_matrix[q])
# else:
# ss=len(distribution_matrix[q])
# combine_list=[]
# for ii in range(ss):
# combine_list.append(str(distribution_matrix[q][ii])+'('+str(test_label_matrix[q][ii])+')')
# print combine_list
# exit(0)
# print 'distribution_matrix[q]:',distribution_matrix[q]
pred_ans=decode_predict_id(batch_predict_ids[q], test_para_wordlist_list[q])
q_id=q_ids_batch[q]
pred_dict[q_id]=pred_ans
# writefile.write('"'+str(q_id)+'": "'+pred_ans+'", ')
# pred_ans=extract_ansList_attentionList(test_para_wordlist_list[q], distribution_matrix[q], numpy.asarray(paralist_extra_features[q], dtype=theano.config.floatX), sub_para_mask[q], Q_list_inword[q])
# q_gold_ans_set=para_gold_ansset_list[q]
# # print test_para_wordlist_list[q]
# # print Q_list_inword[q]
# # print pred_ans.encode('utf8'), q_gold_ans_set
# if pred_ans in q_gold_ans_set:
# exact_match+=1
# F1=MacroF1(pred_ans, q_gold_ans_set)
# F1_match+=F1
with codecs.open(rootPath+'predictions.txt', 'w', 'utf-8') as outfile:
json.dump(pred_dict, outfile)
F1_acc, exact_acc = standard_eval(rootPath+'dev-v1.1.json', rootPath+'predictions.txt')
# F1_acc=F1_match/q_amount
# exact_acc=exact_match/q_amount
if F1_acc> max_F1_acc:
max_F1_acc=F1_acc
if exact_acc> max_exact_acc:
max_exact_acc=exact_acc
if max_exact_acc > max_EM:
store_model_to_file(rootPath+'Best_Paras_conv_'+str(max_exact_acc), params)
print 'Finished storing best params at:', max_exact_acc
print 'current average F1:', F1_acc, '\t\tmax F1:', max_F1_acc, 'current exact:', exact_acc, '\t\tmax exact_acc:', max_exact_acc
# os.system('python evaluate-v1.1.py '+rootPath+'dev-v1.1.json '+rootPath+'predictions.txt')
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.1, n_epochs=2000, word_nkerns=500, char_nkerns=100, batch_size=1, window_width=3,
emb_size=500, char_emb_size=100, hidden_size=200,
margin=0.5, L2_weight=0.0003, update_freq=1, norm_threshold=5.0, max_truncate=40,
max_char_len=40, max_des_len=20, max_relation_len=5, max_Q_len=30, train_neg_size=6,
neg_all=100, train_size=75893, test_size=19168, mark='_BiasedMaxPool_lr0.1_word500_char100_noDes_ent2.0'): #train_size=75909, test_size=17386
# maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/freebase/SimpleQuestions_v2/'
triple_files=['annotated_fb_data_train.entitylinking.top20_succSet_asInput.txt', 'annotated_fb_data_test.entitylinking.top20_succSet_asInput.fromMo_FB5M.txt']
rng = numpy.random.RandomState(23455)
word2id, char2id=load_word2id_char2id(mark)
# datasets, datasets_test, length_per_example_test, vocab_size, char_size=load_test_or_valid(triple_files[0], triple_files[1], max_char_len, max_des_len, max_relation_len, max_Q_len, train_size, test_size)#max_char_len, max_des_len, max_relation_len, max_Q_len
datasets_test, length_per_example_test, word2id, char2id = load_test_or_valid(triple_files[1], char2id, word2id, max_char_len, max_des_len, max_relation_len, max_Q_len, test_size)
vocab_size=len(word2id)
char_size=len(char2id)
print 'vocab_size:', vocab_size, 'char_size:', char_size
# train_data=datasets
# valid_data=datasets[1]
test_data=datasets_test
# result=(pos_entity_char, pos_entity_des, relations, entity_char_lengths, entity_des_lengths, relation_lengths, mention_char_ids, remainQ_word_ids, mention_char_lens, remainQ_word_lens, entity_scores)
#
# train_pos_entity_char=train_data[0]
# train_pos_entity_des=train_data[1]
# train_relations=train_data[2]
# train_entity_char_lengths=train_data[3]
# train_entity_des_lengths=train_data[4]
# train_relation_lengths=train_data[5]
# train_mention_char_ids=train_data[6]
# train_remainQ_word_ids=train_data[7]
# train_mention_char_lens=train_data[8]
# train_remainQ_word_len=train_data[9]
# train_entity_scores=train_data[10]
test_pos_entity_char=test_data[0]
# test_pos_entity_des=test_data[1]
test_relations=test_data[2]
test_entity_char_lengths=test_data[3]
# test_entity_des_lengths=test_data[4]
test_relation_lengths=test_data[5]
test_mention_char_ids=test_data[6]
test_remainQ_word_ids=test_data[7]
test_mention_char_lens=test_data[8]
test_remainQ_word_len=test_data[9]
test_entity_scores=test_data[10]
#
# test_pos_entity_char=test_data[0] #matrix, each row for line example, all head and tail entity, iteratively: 40*2*51
# test_pos_entity_des=test_data[1] #matrix, each row for a examle: 20*2*51
# test_relations=test_data[2] #matrix, each row for a example: 5*51
# test_entity_char_lengths=test_data[3] #matrix, each row for a example: 3*2*51 (three valies for one entity)
# test_entity_des_lengths=test_data[4] #matrix, each row for a example: 3*2*51 (three values for one entity)
# test_relation_lengths=test_data[5] #matrix, each row for a example: 3*51
# test_mention_char_ids=test_data[6] #matrix, each row for a mention: 40
# test_remainQ_word_ids=test_data[7] #matrix, each row for a question: 30
# test_mention_char_lens=test_data[8] #matrix, each three values for a mention: 3
# test_remainQ_word_len=test_data[9] #matrix, each three values for a remain question: 3
# train_sizes=[len(train_pos_entity_char), len(train_pos_entity_des), len(train_relations), len(train_entity_char_lengths), len(train_entity_des_lengths),\
# len(train_relation_lengths), len(train_mention_char_ids), len(train_remainQ_word_ids), len(train_mention_char_lens), len(train_remainQ_word_len), len(train_entity_scores)]
# if sum(train_sizes)/len(train_sizes)!=train_size:
# print 'weird size:', train_sizes
# exit(0)
test_sizes=[len(test_pos_entity_char), len(test_relations), len(test_entity_char_lengths),\
len(test_relation_lengths), len(test_mention_char_ids), len(test_remainQ_word_ids), len(test_mention_char_lens), len(test_remainQ_word_len), len(test_entity_scores)]
if sum(test_sizes)/len(test_sizes)!=test_size:
print 'weird size:', test_sizes
exit(0)
# n_train_batches=train_size/batch_size
# n_test_batches=test_size/batch_size
# train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
# test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_pos_entity_char=pythonList_into_theanoIntMatrix(train_pos_entity_char)
# indices_train_pos_entity_des=pythonList_into_theanoIntMatrix(train_pos_entity_des)
# indices_train_relations=pythonList_into_theanoIntMatrix(train_relations)
# indices_train_entity_char_lengths=pythonList_into_theanoIntMatrix(train_entity_char_lengths)
# indices_train_entity_des_lengths=pythonList_into_theanoIntMatrix(train_entity_des_lengths)
# indices_train_relation_lengths=pythonList_into_theanoIntMatrix(train_relation_lengths)
# indices_train_mention_char_ids=pythonList_into_theanoIntMatrix(train_mention_char_ids)
# indices_train_remainQ_word_ids=pythonList_into_theanoIntMatrix(train_remainQ_word_ids)
# indices_train_mention_char_lens=pythonList_into_theanoIntMatrix(train_mention_char_lens)
# indices_train_remainQ_word_len=pythonList_into_theanoIntMatrix(train_remainQ_word_len)
# indices_train_entity_scores=pythonList_into_theanoFloatMatrix(train_entity_scores)
# indices_test_pos_entity_char=pythonList_into_theanoIntMatrix(test_pos_entity_char)
# indices_test_pos_entity_des=pythonList_into_theanoIntMatrix(test_pos_entity_des)
# indices_test_relations=pythonList_into_theanoIntMatrix(test_relations)
# indices_test_entity_char_lengths=pythonList_into_theanoIntMatrix(test_entity_char_lengths)
# indices_test_entity_des_lengths=pythonList_into_theanoIntMatrix(test_entity_des_lengths)
# indices_test_relation_lengths=pythonList_into_theanoIntMatrix(test_relation_lengths)
# indices_test_mention_char_ids=pythonList_into_theanoIntMatrix(test_mention_char_ids)
# indices_test_remainQ_word_ids=pythonList_into_theanoIntMatrix(test_remainQ_word_ids)
# indices_test_mention_char_lens=pythonList_into_theanoIntMatrix(test_mention_char_lens)
# indices_test_remainQ_word_len=pythonList_into_theanoIntMatrix(test_remainQ_word_len)
# indices_test_entity_scores=pythonList_into_theanoIntMatrix(test_entity_scores)
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
# rand_values=load_word2vec_to_init(rand_values, rootPath+'word_emb.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
char_rand_values=random_value_normal((char_size+1, char_emb_size), theano.config.floatX, numpy.random.RandomState(1234))
# char_rand_values[0]=numpy.array(numpy.zeros(char_emb_size),dtype=theano.config.floatX)
char_embeddings=theano.shared(value=char_rand_values, borrow=True)
# allocate symbolic variables for the data
index = T.iscalar()
chosed_indices=T.ivector()
ent_char_ids_M = T.imatrix()
ent_lens_M = T.imatrix()
men_char_ids_M = T.imatrix()
men_lens_M=T.imatrix()
rel_word_ids_M=T.imatrix()
rel_word_lens_M=T.imatrix()
#desH_word_ids_M=T.imatrix()
#desH_word_lens_M=T.imatrix()
q_word_ids_M=T.imatrix()
q_word_lens_M=T.imatrix()
ent_scores=T.fvector()
filter_size=(emb_size,window_width)
char_filter_size=(char_emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
char_filter_shape=(char_nkerns, 1, char_filter_size[0], char_filter_size[1])
word_filter_shape=(word_nkerns, 1, filter_size[0], filter_size[1])
char_conv_W, char_conv_b=create_conv_para(rng, filter_shape=char_filter_shape)
q_rel_conv_W, q_rel_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
#q_desH_conv_W, q_desH_conv_b=create_conv_para(rng, filter_shape=word_filter_shape)
params = [char_embeddings, embeddings, char_conv_W, char_conv_b, q_rel_conv_W, q_rel_conv_b]#, q_desH_conv_W, q_desH_conv_b]
load_model_from_file(rootPath, params, mark)
def SimpleQ_matches_Triple(ent_char_ids_f,ent_lens_f,rel_word_ids_f,rel_word_lens_f,
men_char_ids_f, q_word_ids_f, men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng, input=ent_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
men_char_conv = Conv_with_input_para(rng, input=men_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
#q_desH
#q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
#desH_conv = Conv_with_input_para(rng, input=desH_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
ent_conv_pool=Max_Pooling(rng, input_l=ent_char_conv.output, left_l=ent_lens_f[0], right_l=ent_lens_f[2])
men_conv_pool=Max_Pooling(rng, input_l=men_char_conv.output, left_l=men_lens_f[0], right_l=men_lens_f[2])
#q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
#q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
#desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)*0.33333+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)*0.55
# 0.0*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
simi_list, updates = theano.scan(
SimpleQ_matches_Triple,
sequences=[ent_char_ids_M,ent_lens_M,rel_word_ids_M,rel_word_lens_M,
men_char_ids_M, q_word_ids_M, men_lens_M, q_word_lens_M])
simi_list+=0.2*ent_scores
posi_simi=simi_list[0]
nega_simies=simi_list[1:]
loss_simi_list=T.maximum(0.0, margin-posi_simi.reshape((1,1))+nega_simies)
loss_simi=T.sum(loss_simi_list)
test_model = theano.function([ent_char_ids_M, ent_lens_M, men_char_ids_M, men_lens_M, rel_word_ids_M, rel_word_lens_M,
q_word_ids_M, q_word_lens_M, ent_scores], [loss_simi, simi_list],on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... testing'
start_time = time.clock()
mid_time = start_time
epoch = 0
test_loss=[]
succ=0
for i in range(test_size):
#prepare data
test_ent_char_ids_M= numpy.asarray(test_pos_entity_char[i], dtype='int32').reshape((length_per_example_test[i], max_char_len))
test_ent_lens_M = numpy.asarray(test_entity_char_lengths[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_men_char_ids_M = numpy.asarray(test_mention_char_ids[i], dtype='int32').reshape((length_per_example_test[i], max_char_len))
test_men_lens_M = numpy.asarray(test_mention_char_lens[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_rel_word_ids_M = numpy.asarray(test_relations[i], dtype='int32').reshape((length_per_example_test[i], max_relation_len))
test_rel_word_lens_M = numpy.asarray(test_relation_lengths[i], dtype='int32').reshape((length_per_example_test[i], 3))
#test_desH_word_ids_M =numpy.asarray( test_pos_entity_des[i], dtype='int32').reshape((length_per_example_test[i], max_des_len))
#test_desH_word_lens_M = numpy.asarray(test_entity_des_lengths[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_q_word_ids_M = numpy.asarray(test_remainQ_word_ids[i], dtype='int32').reshape((length_per_example_test[i], max_Q_len))
test_q_word_lens_M = numpy.asarray(test_remainQ_word_len[i], dtype='int32').reshape((length_per_example_test[i], 3))
test_ent_scores = numpy.asarray(test_entity_scores[i], dtype=theano.config.floatX)
loss_simi_i,simi_list_i=test_model(test_ent_char_ids_M, test_ent_lens_M, test_men_char_ids_M, test_men_lens_M, test_rel_word_ids_M, test_rel_word_lens_M,
test_q_word_ids_M, test_q_word_lens_M, test_ent_scores)
# print 'simi_list_i:', simi_list_i[:10]
test_loss.append(loss_simi_i)
if len(simi_list_i)==1 or simi_list_i[0]>=max(simi_list_i[1:]):
succ+=1
if i%1000==0:
print 'testing', i, '...acc:', (succ*1.0/(i+1))*(19168*1.0/21687)
succ=succ*100.0/21687
#now, check MAP and MRR
print 'accu:', succ
# store_model_to_file(rootPath, params, succ, mark)
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
def evaluate_lenet5(learning_rate=0.09, n_epochs=2000, nkerns=[50,50], batch_size=1, window_width=3,
maxSentLength=64, maxDocLength=60, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.00065, update_freq=1, norm_threshold=5.0, max_s_length=57, max_d_length=59):
maxSentLength=max_s_length+2*(window_width-1)
maxDocLength=max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data,train_size, test_data, test_size, vocab_size=load_MCTest_corpus(rootPath+'vocab.txt', rootPath+'mc500.train.tsv_standardlized.txt', rootPath+'mc500.test.tsv_standardlized.txt', max_s_length,maxSentLength, maxDocLength)#vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
[train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A,
train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A]=train_data
[test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A,
test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A]=test_data
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A= T.lvector()
y = T.lvector()
len_D=T.lscalar()
len_D_s=T.lvector()
len_Q=T.lscalar()
len_A=T.lscalar()
left_D=T.lscalar()
left_D_s=T.lvector()
left_Q=T.lscalar()
left_A=T.lscalar()
right_D=T.lscalar()
right_D_s=T.lvector()
right_Q=T.lscalar()
right_A=T.lscalar()
#wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words=(emb_size,window_width)
filter_sents=(nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_Q_input = embeddings[index_Q.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A_input = embeddings[index_A.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]))
# load_model_for_conv1([conv_W, conv_b])
layer0_D = Conv_with_input_para(rng, input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A = Conv_with_input_para(rng, input=layer0_A_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_D_output=debug_print(layer0_D.output, 'layer0_D.output')
layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A_output=debug_print(layer0_A.output, 'layer0_A.output')
layer0_para=[conv_W, conv_b]
layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A, right_r=right_A,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA = Conv_with_input_para(rng, input=layer1_DA.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
#conv single Q and A into doc level with same conv weights
layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A_output_sent_rep_Dlevel=debug_print(layer2_A.output_sent_rep_Dlevel, 'layer2_A.output_sent_rep_Dlevel')
layer2_para=[conv2_W, conv2_b]
layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA=Average_Pooling_for_Top(rng, input_l=layer2_DA.output, input_r=layer2_A_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
#high-way
high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1])
transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA.output_D_sent_level_rep) + high_b), 'transform_gate_DA')
transform_gate_Q=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b), 'transform_gate_Q')
transform_gate_A=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA.output_QA_sent_level_rep) + high_b), 'transform_gate_A')
highW_para=[high_W, high_b]
overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A=(1.0-transform_gate_DA)*layer1_DA.output_D_sent_level_rep+transform_gate_DA*layer3_DA.output_D_doc_level_rep
overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A=(1.0-transform_gate_A)*layer1_DA.output_QA_sent_level_rep+transform_gate_A*layer2_A.output_sent_rep_Dlevel
simi_sent_level=debug_print(cosine(layer1_DQ.output_D_sent_level_rep+layer1_DA.output_D_sent_level_rep, layer1_DQ.output_QA_sent_level_rep+layer1_DA.output_QA_sent_level_rep), 'simi_sent_level')
simi_doc_level=debug_print(cosine(layer3_DQ.output_D_doc_level_rep+layer3_DA.output_D_doc_level_rep, layer2_Q.output_sent_rep_Dlevel+layer2_A.output_sent_rep_Dlevel), 'simi_doc_level')
simi_overall_level=debug_print(cosine(overall_D_Q+overall_D_A, overall_Q+overall_A), 'simi_overall_level')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
layer4_input=debug_print(T.concatenate([simi_sent_level,
simi_doc_level,
simi_overall_level
], axis=1), 'layer4_input')#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4=LogisticRegression(rng, input=layer4_input, n_in=3, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer4.W** 2).sum()+(high_W**2).sum()+(conv2_W**2).sum()+(conv_W**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer4.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
#
# [train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
# train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A,
# train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
# train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A]=train_data
# [test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
# test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A,
# test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
# test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A]=test_data
# index = T.lscalar()
# index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
# index_A= T.lvector()
#
# y = T.lvector()
# len_D=T.lscalar()
# len_D_s=T.lvector()
# len_Q=T.lscalar()
# len_A=T.lscalar()
#
# left_D=T.lscalar()
# left_D_s=T.lvector()
# left_Q=T.lscalar()
# left_A=T.lscalar()
#
# right_D=T.lscalar()
# right_D_s=T.lvector()
# right_Q=T.lscalar()
# right_A=T.lscalar()
#
#
# #wmf=T.dmatrix()
# cost_tmp=T.dscalar()
test_model = theano.function([index], [layer4.errors(y),layer4_input, y, layer4.prop_for_posi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A: test_data_A[index],
y: test_Y[index:index+batch_size],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A: test_Length_A[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A: test_leftPad_A[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A: test_rightPad_A[index]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params+layer2_para+layer0_para+highW_para
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index+batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer4.errors(y), layer4_input, y],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index+batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
#shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
# readfile=open('/mounts/data/proj/wenpeng/Dataset/SICK/train_plus_dev.txt', 'r')
# train_pairs=[]
# train_y=[]
# for line in readfile:
# tokens=line.strip().split('\t')
# listt=tokens[0]+'\t'+tokens[1]
# train_pairs.append(listt)
# train_y.append(tokens[2])
# readfile.close()
# writefile=open('/mounts/data/proj/wenpeng/Dataset/SICK/weights_fine_tune.txt', 'w')
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
sys.stdout.write( "Training :[%6f] %% complete!\r" % (batch_start*100.0/train_size) )
sys.stdout.flush()
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
test_prop=[]
for i in test_batch_start:
test_loss, layer3_input, y, posi_prop=test_model(i)
test_prop.append(posi_prop[0][0])
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc=compute_test_acc(test_y, test_prop)
#test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.decision_function(test_features)
lr=linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr=lr.decision_function(test_features)
acc_svm=compute_test_acc(test_y, results)
acc_lr=compute_test_acc(test_y, results_lr)
find_better=False
if acc_svm > max_acc:
max_acc=acc_svm
best_epoch=epoch
find_better=True
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
find_better=True
if acc_lr> max_acc:
max_acc=acc_lr
best_epoch=epoch
find_better=True
print '\t\t\tsvm:', acc_svm, 'lr:', acc_lr, 'nn:', test_acc, 'max:', max_acc,'(at',best_epoch,')'
# if find_better==True:
# store_model_to_file(layer2_para, best_epoch)
# print 'Finished storing best conv params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
nkerns=[256, 256],
batch_size=1,
window_width=3,
maxSentLength=64,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.0006,
update_freq=1,
norm_threshold=5.0,
max_truncate=33): # max_truncate can be 45
maxSentLength = max_truncate + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/SICK/'
rng = numpy.random.RandomState(23455)
# datasets, vocab_size=load_SICK_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'train.txt', rootPath+'test.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test
datasets, vocab_size = load_SICK_corpus(rootPath + 'vocab.txt',
rootPath + 'train_plus_dev.txt',
rootPath + 'test.txt',
max_truncate,
maxSentLength,
entailment=True)
mt_train, mt_test = load_mts_wikiQA(
rootPath + 'Train_plus_dev_MT/concate_14mt_train.txt',
rootPath + 'Test_MT/concate_14mt_test.txt')
extra_train, extra_test = load_extra_features(
rootPath +
'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt',
rootPath +
'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt'
)
discri_train, discri_test = load_extra_features(
rootPath + 'train_plus_dev_discri_features_0.3.txt',
rootPath + 'test_discri_features_0.3.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int64')
indices_train_r = T.cast(indices_train_r, 'int64')
indices_test_l = T.cast(indices_test_l, 'int64')
indices_test_r = T.cast(indices_test_r, 'int64')
'''
indices_train_l=T.cast(indices_train_l, 'int32')
indices_train_r=T.cast(indices_train_r, 'int32')
indices_test_l=T.cast(indices_test_l, 'int32')
indices_test_r=T.cast(indices_test_r, 'int32')
'''
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
# rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_glove_50d.txt')
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_glove_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l = T.lscalar()
right_l = T.lscalar()
left_r = T.lscalar()
right_r = T.lscalar()
length_l = T.lscalar()
length_r = T.lscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
extra = T.dmatrix()
discri = T.dmatrix()
cost_tmp = T.dscalar()
# #GPU
# index = T.iscalar()
# x_index_l = T.imatrix('x_index_l') # now, x is the index matrix, must be integer
# x_index_r = T.imatrix('x_index_r')
# y = T.ivector('y')
# left_l=T.iscalar()
# right_l=T.iscalar()
# left_r=T.iscalar()
# right_r=T.iscalar()
# length_l=T.iscalar()
# length_r=T.iscalar()
# norm_length_l=T.fscalar()
# norm_length_r=T.fscalar()
# #mts=T.dmatrix()
# #wmf=T.dmatrix()
# cost_tmp=T.fscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = debug_print(
embeddings[x_index_l.flatten()].reshape(
(maxSentLength, emb_size)).transpose(), 'layer0_l_input')
layer0_r_input = debug_print(
embeddings[x_index_r.flatten()].reshape(
(maxSentLength, emb_size)).transpose(), 'layer0_r_input')
l_input_tensor = debug_print(
Matrix_Bit_Shift(layer0_l_input[:, left_l:-right_l]), 'l_input_tensor')
r_input_tensor = debug_print(
Matrix_Bit_Shift(layer0_r_input[:, left_r:-right_r]), 'r_input_tensor')
addition_l = T.sum(layer0_l_input[:, left_l:-right_l], axis=1)
addition_r = T.sum(layer0_r_input[:, left_r:-right_r], axis=1)
cosine_addition = cosine(addition_l, addition_r)
eucli_addition = 1.0 / (1.0 + EUCLID(addition_l, addition_r)) #25.2%
U, W, b = create_GRU_para(rng, emb_size, nkerns[0])
layer0_para = [U, W, b]
layer0_A1 = GRU_Batch_Tensor_Input(X=l_input_tensor,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
bptt_truncate=-1)
layer0_A2 = GRU_Batch_Tensor_Input(X=r_input_tensor,
hidden_dim=nkerns[0],
U=U,
W=W,
b=b,
bptt_truncate=-1)
cosine_sent = cosine(layer0_A1.output_sent_rep, layer0_A2.output_sent_rep)
eucli_sent = 1.0 / (1.0 + EUCLID(layer0_A1.output_sent_rep,
layer0_A2.output_sent_rep)) #25.2%
#ibm attentive pooling at extended sentence level
attention_matrix = compute_simi_feature_matrix_with_matrix(
layer0_A1.output_matrix, layer0_A2.output_matrix, layer0_A1.dim,
layer0_A2.dim,
maxSentLength * (maxSentLength + 1) / 2)
# attention_vec_l_extended=T.nnet.softmax(T.max(attention_matrix, axis=1)).transpose()
# ibm_l_extended=layer0_A1.output_matrix.dot(attention_vec_l_extended).transpose()
# attention_vec_r_extended=T.nnet.softmax(T.max(attention_matrix, axis=0)).transpose()
# ibm_r_extended=layer0_A2.output_matrix.dot(attention_vec_r_extended).transpose()
# cosine_ibm_extended=cosine(ibm_l_extended, ibm_r_extended)
# eucli_ibm_extended=1.0/(1.0+EUCLID(ibm_l_extended, ibm_r_extended))#25.2%
#ibm attentive pooling at original sentence level
simi_matrix_sent = compute_simi_feature_matrix_with_matrix(
layer0_A1.output_sent_hiddenstates, layer0_A2.output_sent_hiddenstates,
length_l, length_r, maxSentLength)
attention_vec_l = T.nnet.softmax(T.max(simi_matrix_sent,
axis=1)).transpose()
ibm_l = layer0_A1.output_sent_hiddenstates.dot(attention_vec_l).transpose()
attention_vec_r = T.nnet.softmax(T.max(simi_matrix_sent,
axis=0)).transpose()
ibm_r = layer0_A2.output_sent_hiddenstates.dot(attention_vec_r).transpose()
cosine_ibm = cosine(ibm_l, ibm_r)
eucli_ibm = 1.0 / (1.0 + EUCLID(ibm_l, ibm_r)) #25.2%
l_max_attention = T.max(attention_matrix, axis=1)
neighborsArgSorted = T.argsort(l_max_attention)
kNeighborsArg = neighborsArgSorted[:3] #only average the min 3 vectors
ll = T.sort(kNeighborsArg).flatten() # make y indices in acending lie
r_max_attention = T.max(attention_matrix, axis=0)
neighborsArgSorted_r = T.argsort(r_max_attention)
kNeighborsArg_r = neighborsArgSorted_r[:3] #only average the min 3 vectors
rr = T.sort(kNeighborsArg_r).flatten() # make y indices in acending lie
l_max_min_attention = debug_print(layer0_A1.output_matrix[:, ll],
'l_max_min_attention')
r_max_min_attention = debug_print(layer0_A2.output_matrix[:, rr],
'r_max_min_attention')
U1, W1, b1 = create_GRU_para(rng, nkerns[0], nkerns[1])
layer1_para = [U1, W1, b1]
layer1_A1 = GRU_Matrix_Input(X=l_max_min_attention,
word_dim=nkerns[0],
hidden_dim=nkerns[1],
U=U1,
W=W1,
b=b1,
bptt_truncate=-1)
layer1_A2 = GRU_Matrix_Input(X=r_max_min_attention,
word_dim=nkerns[0],
hidden_dim=nkerns[1],
U=U1,
W=W1,
b=b1,
bptt_truncate=-1)
vec_l = debug_print(layer1_A1.output_vector_last.reshape((1, nkerns[1])),
'vec_l')
vec_r = debug_print(layer1_A2.output_vector_last.reshape((1, nkerns[1])),
'vec_r')
# sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
# aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
# norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
# sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
# aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
# norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
#
uni_cosine = cosine(vec_l, vec_r)
# aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
# uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
# '''
# linear=Linear(sum_uni_l, sum_uni_r)
# poly=Poly(sum_uni_l, sum_uni_r)
# sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
# rbf=RBF(sum_uni_l, sum_uni_r)
# gesd=GESD(sum_uni_l, sum_uni_r)
# '''
eucli_1 = 1.0 / (1.0 + EUCLID(vec_l, vec_r)) #25.2%
# #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
#
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
#
# '''
# len_l=length_l.reshape((1,1))
# len_r=length_r.reshape((1,1))
# '''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input = T.concatenate(
[
vec_l,
vec_r,
uni_cosine,
eucli_1,
cosine_addition,
eucli_addition,
# cosine_sent, eucli_sent,
ibm_l.reshape((1, nkerns[0])),
ibm_r.reshape((1, nkerns[0])), #2*nkerns[0]+
cosine_ibm,
eucli_ibm,
# ibm_l_extended.reshape((1, nkerns[0])), ibm_r_extended.reshape((1, nkerns[0])), #2*nkerns[0]+
# cosine_ibm_extended, eucli_ibm_extended,
mts,
len_l,
len_r,
extra
],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3 = LogisticRegression(rng,
input=layer3_input,
n_in=(2 * nkerns[1] + 2) + 2 +
(2 * nkerns[0] + 2) + 14 + 2 + 9,
n_out=3)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer3.W**2).sum() + (U**2).sum() + (W**2).sum() + (U1**2).sum() +
(W1**2).sum(), 'L2_reg') #+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer3.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index], [layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size],
extra: extra_test[index:index + batch_size],
discri: discri_test[index:index + batch_size]
},
on_unused_input='ignore',
allow_input_downcast=True)
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params + layer1_para + layer0_para #+[embeddings]# + layer1.params
# params_conv = [conv_W, conv_b]
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
# updates = []
# grads = T.grad(cost, params)
# i = theano.shared(numpy.float64(0.))
# i_t = i + 1.
# fix1 = 1. - (1. - b1)**i_t
# fix2 = 1. - (1. - b2)**i_t
# lr_t = lr * (T.sqrt(fix2) / fix1)
# for p, g in zip(params, grads):
# m = theano.shared(p.get_value() * 0.)
# v = theano.shared(p.get_value() * 0.)
# m_t = (b1 * g) + ((1. - b1) * m)
# v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
# g_t = m_t / (T.sqrt(v_t) + e)
# p_t = p - (lr_t * g_t)
# updates.append((m, m_t))
# updates.append((v, v_t))
# updates.append((p, p_t))
# updates.append((i, i_t))
# return updates
#
# updates=Adam(cost=cost, params=params, lr=learning_rate)
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
extra: extra_train[index:index + batch_size],
discri: discri_train[index:index + batch_size]
},
on_unused_input='ignore',
allow_input_downcast=True)
train_model_predict = theano.function(
[index, cost_tmp],
[cost_this, layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
extra: extra_train[index:index + batch_size],
discri: discri_train[index:index + batch_size]
},
on_unused_input='ignore',
allow_input_downcast=True)
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.time()
mid_time = start_time
epoch = 0
done_looping = False
acc_max = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
# shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
# if (batch_start+1)%1000==0:
# print batch_start+1, 'uses ', (time.time()-mid_time)/60.0, 'min'
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start, 0.0)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses = []
test_y = []
test_features = []
for i in test_batch_start:
test_loss, layer3_input, y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
print(
('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') % (epoch, minibatch_index, n_train_batches,
(1 - test_score) * 100.))
acc_nn = 1 - test_score
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
#this step is risky: if the training data is too big, then this step will make the training time twice longer
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start, 0.0)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results = clf.predict(test_features)
lr = linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr = lr.predict(test_features)
corr_count = 0
corr_count_lr = 0
test_size = len(test_y)
for i in range(test_size):
if results[i] == test_y[i]:
corr_count += 1
if results_lr[i] == test_y[i]:
corr_count_lr += 1
acc_svm = corr_count * 1.0 / test_size
acc_lr = corr_count_lr * 1.0 / test_size
if acc_svm > acc_max:
acc_max = acc_svm
best_epoch = epoch
if acc_lr > acc_max:
acc_max = acc_lr
best_epoch = epoch
if acc_nn > acc_max:
acc_max = acc_nn
best_epoch = epoch
print 'acc_nn:', acc_nn, 'acc_lr:', acc_lr, 'acc_svm:', acc_svm, ' max acc: ', acc_max, ' at epoch: ', best_epoch
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(file_name,
input_filename,
model_filename,
learning_rate=0.001,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=128,
max_d_length=128,
margin=0.3):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
f = open(file_name, 'w')
f.write("model options " + str(model_options) + '\n')
#rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data, _train_Label, train_size, test_data, _test_Label, test_size, vocab_size = load_MCTest_corpus_DPN(
'vocab_table_wenyan.txt', input_filename, input_filename, max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
f.write('train_size : ' + str(train_size))
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[
train_data_D, train_data_A1, train_Label, train_Length_D,
train_Length_D_s, train_Length_A1, train_leftPad_D, train_leftPad_D_s,
train_leftPad_A1, train_rightPad_D, train_rightPad_D_s,
train_rightPad_A1
] = train_data
[
test_data_D, test_data_A1, test_Label, test_Length_D, test_Length_D_s,
test_Length_A1, test_leftPad_D, test_leftPad_D_s, test_leftPad_A1,
test_rightPad_D, test_rightPad_D_s, test_rightPad_A1
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values, 'vectors_wenyan2.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1 = T.lvector()
# index_A2= T.lvector()
# index_A3= T.lvector()
# index_A4= T.lvector()
y = T.lscalar()
len_D = T.lscalar()
len_D_s = T.lvector()
# len_Q=T.lscalar()
len_A1 = T.lscalar()
# len_A2=T.lscalar()
# len_A3=T.lscalar()
# len_A4=T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
# left_Q=T.lscalar()
left_A1 = T.lscalar()
# left_A2=T.lscalar()
# left_A3=T.lscalar()
# left_A4=T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
# right_Q=T.lscalar()
right_A1 = T.lscalar()
# right_A2=T.lscalar()
# right_A3=T.lscalar()
# right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
f.write('... building the model\n')
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#layer0_A2_input = embeddings[index_A2.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A3_input = embeddings[index_A3.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A4_input = embeddings[index_A4.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(
rng, nkerns[0], nkerns[1]
) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
#layer0_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A3 = Conv_with_input_para(rng, input=layer0_A3_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A4 = Conv_with_input_para(rng, input=layer0_A4_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
#layer0_A2_output=debug_print(layer0_A2.output, 'layer0_A2.output')
# layer0_A3_output=debug_print(layer0_A3.output, 'layer0_A3.output')
# layer0_A4_output=debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=1)
#layer1_DA2=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A2_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A2, right_r=right_A2,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A2+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA3=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A3_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A3, right_r=right_A3,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A3+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA4=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A4_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A4, right_r=right_A4,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A4+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#layer2_DA2 = Conv_with_input_para(rng, input=layer1_DA2.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA3 = Conv_with_input_para(rng, input=layer1_DA3.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA4 = Conv_with_input_para(rng, input=layer1_DA4.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#layer2_A2 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA2.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A3 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA3.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A4 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA4.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
# layer2_A2_output_sent_rep_Dlevel=debug_print(layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
# layer2_A3_output_sent_rep_Dlevel=debug_print(layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
# layer2_A4_output_sent_rep_Dlevel=debug_print(layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=1)
#layer3_DA2=Average_Pooling_for_Top(rng, input_l=layer2_DA2.output, input_r=layer2_A2_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA3=Average_Pooling_for_Top(rng, input_l=layer2_DA3.output, input_r=layer2_A3_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA4=Average_Pooling_for_Top(rng, input_l=layer2_DA4.output, input_r=layer2_A4_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
# transform_gate_A2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b), 'transform_gate_A2')
# transform_gate_A3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b), 'transform_gate_A3')
# transform_gate_A4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b), 'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
# overall_D_A2=(1.0-transform_gate_DA2)*layer1_DA2.output_D_sent_level_rep+transform_gate_DA2*layer3_DA2.output_D_doc_level_rep
# overall_D_A3=(1.0-transform_gate_DA3)*layer1_DA3.output_D_sent_level_rep+transform_gate_DA3*layer3_DA3.output_D_doc_level_rep
# overall_D_A4=(1.0-transform_gate_DA4)*layer1_DA4.output_D_sent_level_rep+transform_gate_DA4*layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
#overall_A2=(1.0-transform_gate_A2)*layer1_DA2.output_QA_sent_level_rep+transform_gate_A2*layer2_A2.output_sent_rep_Dlevel
# overall_A3=(1.0-transform_gate_A3)*layer1_DA3.output_QA_sent_level_rep+transform_gate_A3*layer2_A3.output_sent_rep_Dlevel
# overall_A4=(1.0-transform_gate_A4)*layer1_DA4.output_QA_sent_level_rep+transform_gate_A4*layer2_A4.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
#simi_sent_level2=debug_print(cosine(layer1_DA2.output_D_sent_level_rep, layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
# simi_sent_level3=debug_print(cosine(layer1_DA3.output_D_sent_level_rep, layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
# simi_sent_level4=debug_print(cosine(layer1_DA4.output_D_sent_level_rep, layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
#simi_doc_level2=debug_print(cosine(layer3_DA2.output_D_doc_level_rep, layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
# simi_doc_level3=debug_print(cosine(layer3_DA3.output_D_doc_level_rep, layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
# simi_doc_level4=debug_print(cosine(layer3_DA4.output_D_doc_level_rep, layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
#simi_overall_level2=debug_print(cosine(overall_D_A2, overall_A2), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(overall_D_A3, overall_A3), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(overall_D_A4, overall_A4), 'simi_overall_level4')
# simi_1=simi_overall_level1+simi_sent_level1+simi_doc_level1
# simi_2=simi_overall_level2+simi_sent_level2+simi_doc_level2
simi_1 = (simi_overall_level1 + simi_sent_level1 + simi_doc_level1) / 3.0
#simi_1 = simi_doc_level1
#simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
logistic_w, logistic_b = create_logistic_para(rng, 1, 2)
logistic_para = [logistic_w, logistic_b]
sent_w, sent_b = create_logistic_para(rng, 1, 2)
doc_w, doc_b = create_logistic_para(rng, 1, 2)
sent_para = [sent_w, sent_b]
doc_para = [doc_w, doc_b]
params += logistic_para
params += sent_para
params += doc_para
load_model(params, model_filename)
simi_sent = T.dot(sent_w, simi_sent_level1) + sent_b.dimshuffle(0, 'x')
simi_sent = simi_sent.dimshuffle(1, 0)
simi_sent = T.nnet.softmax(simi_sent)
tmp_sent = T.log(simi_sent)
simi_doc = T.dot(doc_w, simi_doc_level1) + doc_b.dimshuffle(0, 'x')
simi_doc = simi_doc.dimshuffle(1, 0)
simi_doc = T.nnet.softmax(simi_doc)
tmp_doc = T.log(simi_doc)
#cost = margin - simi_1
simi_overall = T.dot(logistic_w,
simi_overall_level1) + logistic_b.dimshuffle(0, 'x')
simi_overall = simi_overall.dimshuffle(1, 0)
simi_overall = T.nnet.softmax(simi_overall)
predict = T.argmax(simi_overall, axis=1)
tmp_overall = T.log(simi_overall)
cost = -(tmp_overall[0][y] + tmp_doc[0][y] + tmp_sent[0][y]) / 3.0
L2_reg = (conv2_W**2).sum() + (conv_W**2).sum() + (logistic_w**2).sum() + (
high_W**2).sum()
cost = cost + L2_weight * L2_reg
#simi_1 = [simi_overall,simi_doc,simi_sent]
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)
#cost=T.maximum(0.0, margin+simi_sent_level2-simi_sent_level1)+T.maximum(0.0, margin+simi_doc_level2-simi_doc_level1)+T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)
# posi_simi=simi_1
# nega_simi=simi_2
#L2_reg =debug_print((high_W**2).sum()+(conv2_W**2).sum()+(conv_W**2).sum(), 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
#cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, simi_overall, simi_doc, simi_sent, predict],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
y: test_Label[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
# len_A2: test_Length_A2[index],
# len_A3: test_Length_A3[index],
# len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
# left_A2: test_leftPad_A2[index],
# left_A3: test_leftPad_A3[index],
# left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
},
on_unused_input='ignore')
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function(
[index],
[cost, simi_overall, simi_doc, simi_sent, predict],
updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
# index_A2: train_data_A2[index],
# index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
y: train_Label[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
# len_A2: train_Length_A2[index],
# len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
# left_A2: train_leftPad_A2[index],
# left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
# right_A2: train_rightPad_A2[index]
# right_A3: train_rightPad_A3[index],
# right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
f.write('... training\n')
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
cost, simi_overall, simi_doc, simi_sent, predict = test_model(0)
cost, simi_overall1, simi_doc, simi_sent, predict = test_model(1)
cost, simi_overall2, simi_doc, simi_sent, predict = test_model(2)
cost, simi_overall3, simi_doc, simi_sent, predict = test_model(3)
return simi_overall, simi_overall1, simi_overall2, simi_overall3
'''
