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=0.1, 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=0.085, 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=0.001, 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(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=0.01, 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=0.05, 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=0.09, 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=0.085, 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.))
######################
# 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_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)
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_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
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.))
