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
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
def evaluate_lenet5(file_name,
vocab_file,
train_file,
dev_file,
word2vec_file,
learning_rate=0.001,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=128,
max_d_length=128,
margin=0.3):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
f = open(file_name, 'w')
f.write("model options " + str(model_options) + '\n')
rng = numpy.random.RandomState(23455)
train_data, _train_Label, train_size, test_data, _test_Label, test_size, vocab_size = load_MCTest_corpus_DPN(
vocab_file, train_file, dev_file, max_s_length, maxSentLength,
maxDocLength) #vocab_size contain train, dev and test
f.write('train_size : ' + str(train_size))
[
train_data_D, train_data_A1, train_Label, train_Length_D,
train_Length_D_s, train_Length_A1, train_leftPad_D, train_leftPad_D_s,
train_leftPad_A1, train_rightPad_D, train_rightPad_D_s,
train_rightPad_A1
] = train_data
[
test_data_D, test_data_A1, test_Label, test_Length_D, test_Length_D_s,
test_Length_A1, test_leftPad_D, test_leftPad_D_s, test_leftPad_A1,
test_rightPad_D, test_rightPad_D_s, test_rightPad_A1
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
rand_values = load_word2vec_to_init(rand_values, word2vec_file)
embeddings = theano.shared(value=rand_values, borrow=True)
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_A1 = T.lvector()
y = T.lscalar()
len_D = T.lscalar()
len_D_s = T.lvector()
len_A1 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_A1 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_A1 = T.lscalar()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
######################
# BUILD ACTUAL MODEL #
######################
f.write('... building the model\n')
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(
rng, nkerns[0], nkerns[1]
) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
simi_1 = (simi_overall_level1 + simi_sent_level1 + simi_doc_level1) / 3.0
logistic_w, logistic_b = create_logistic_para(rng, 1, 2)
logistic_para = [logistic_w, logistic_b]
params += logistic_para
simi_1 = T.dot(logistic_w, simi_1) + logistic_b.dimshuffle(0, 'x')
simi_1 = simi_1.dimshuffle(1, 0)
simi_1 = T.nnet.softmax(simi_1)
predict = T.argmax(simi_1, axis=1)
tmp = T.log(simi_1)
cost = T.maximum(0.0, margin + tmp[0][1 - y] - tmp[0][y])
L2_reg = (high_W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum() + (
logistic_w**2).sum()
cost = cost + L2_weight * L2_reg
test_model = theano.function(
[index],
[cost, simi_1, predict],
givens={
index_D: test_data_D[index], #a matrix
index_A1: test_data_A1[index],
y: test_Label[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_A1: test_Length_A1[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_A1: test_leftPad_A1[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_A1: test_rightPad_A1[index],
},
on_unused_input='ignore')
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index], [cost, simi_1, predict],
updates=updates,
givens={
index_D: train_data_D[index],
index_A1: train_data_A1[index],
y: train_Label[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_A1: train_Length_A1[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_A1: train_leftPad_A1[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_A1: train_rightPad_A1[index],
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
f.write('... training\n')
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
simi_train = []
predict_train = []
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
cost_average, simi, predict = train_model(batch_start)
simi_train.append(simi)
predict_train.append(predict)
if iter % 1000 == 0:
f.write('@iter :' + str(iter) + '\n')
if iter % n_train_batches == 0:
corr_train = compute_corr_train(predict_train, _train_Label)
res = 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate: ' + str(
corr_train * 100.0 / train_size) + '\n'
f.write(res)
if iter % validation_frequency == 0 or iter % 20000 == 0:
posi_test_sent = []
nega_test_sent = []
posi_test_doc = []
nega_test_doc = []
posi_test_overall = []
nega_test_overall = []
simi_test = []
predict_test = []
for i in test_batch_start:
cost, simi, predict = test_model(i)
#print simi
#f.write('test_predict : ' + str(predict) + ' test_simi : ' + str(simi) + '\n' )
simi_test.append(simi)
predict_test.append(predict)
corr_test = compute_corr(simi_test, predict_test, f)
test_acc = corr_test * 1.0 / (test_size / 4.0)
res = '\t\t\tepoch ' + str(epoch) + ', minibatch ' + str(
minibatch_index) + ' / ' + str(
n_train_batches) + ' test acc of best model ' + str(
test_acc * 100.0) + '\n'
f.write(res)
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
res = '\t\t\tmax: ' + str(max_acc) + ' (at ' + str(
best_epoch) + ')\n'
f.write(res)
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.05,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59,
margin=0.2):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus_DSSSS(
rootPath + 'vocab_DSSSS.txt', rootPath +
'mc500.train.tsv_standardlized.txt_with_state.txt_DSSSS.txt',
rootPath + 'mc500.test.tsv_standardlized.txt_with_state.txt_DSSSS.txt',
max_s_length, maxSentLength,
maxDocLength) #vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[
train_data_D, train_data_A1, train_data_A2, train_data_A3,
train_data_A4, train_Label, train_Length_D, train_Length_D_s,
train_Length_A1, train_Length_A2, train_Length_A3, train_Length_A4,
train_leftPad_D, train_leftPad_D_s, train_leftPad_A1, train_leftPad_A2,
train_leftPad_A3, train_leftPad_A4, train_rightPad_D,
train_rightPad_D_s, train_rightPad_A1, train_rightPad_A2,
train_rightPad_A3, train_rightPad_A4
] = train_data
[
test_data_D, test_data_A1, test_data_A2, test_data_A3, test_data_A4,
test_Label, test_Length_D, test_Length_D_s, test_Length_A1,
test_Length_A2, test_Length_A3, test_Length_A4, test_leftPad_D,
test_leftPad_D_s, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3,
test_leftPad_A4, test_rightPad_D, test_rightPad_D_s, test_rightPad_A1,
test_rightPad_A2, test_rightPad_A3, test_rightPad_A4
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1 = T.lvector()
index_A2 = T.lvector()
index_A3 = T.lvector()
index_A4 = T.lvector()
# y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
# len_Q=T.lscalar()
len_A1 = T.lscalar()
len_A2 = T.lscalar()
len_A3 = T.lscalar()
len_A4 = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
# left_Q=T.lscalar()
left_A1 = T.lscalar()
left_A2 = T.lscalar()
left_A3 = T.lscalar()
left_A4 = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
# right_Q=T.lscalar()
right_A1 = T.lscalar()
right_A2 = T.lscalar()
right_A3 = T.lscalar()
right_A4 = T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_Q_input = embeddings[index_Q.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A4_input = embeddings[index_A4.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(rng, nkerns[0], nkerns[1])
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A2 = Conv_with_input_para(
rng,
input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A3 = Conv_with_input_para(
rng,
input=layer0_A3_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A4 = Conv_with_input_para(
rng,
input=layer0_A4_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
layer0_A2_output = debug_print(layer0_A2.output, 'layer0_A2.output')
layer0_A3_output = debug_print(layer0_A3.output, 'layer0_A3.output')
layer0_A4_output = debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA2 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A2_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A2,
right_r=right_A2,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A2 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA3 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A3_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A3,
right_r=right_A3,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A3 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA4 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A4_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A4,
right_r=right_A4,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A4 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA2 = Conv_with_input_para(
rng,
input=layer1_DA2.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA3 = Conv_with_input_para(
rng,
input=layer1_DA3.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA4 = Conv_with_input_para(
rng,
input=layer1_DA4.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A2 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA2.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A3 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA3.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A4 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA4.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer2_A2_output_sent_rep_Dlevel = debug_print(
layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
layer2_A3_output_sent_rep_Dlevel = debug_print(
layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
layer2_A4_output_sent_rep_Dlevel = debug_print(
layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA2 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA2.output,
input_r=layer2_A2_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA3 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA3.output,
input_r=layer2_A3_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA4 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA4.output,
input_r=layer2_A4_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_DA2 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA2.output_D_sent_level_rep) + high_b),
'transform_gate_DA2')
transform_gate_DA3 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA3.output_D_sent_level_rep) + high_b),
'transform_gate_DA3')
transform_gate_DA4 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA4.output_D_sent_level_rep) + high_b),
'transform_gate_DA4')
# transform_gate_Q=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b), 'transform_gate_Q')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
transform_gate_A2 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b),
'transform_gate_A2')
transform_gate_A3 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b),
'transform_gate_A3')
transform_gate_A4 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b),
'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
overall_D_A2 = (
1.0 - transform_gate_DA2
) * layer1_DA2.output_D_sent_level_rep + transform_gate_DA2 * layer3_DA2.output_D_doc_level_rep
overall_D_A3 = (
1.0 - transform_gate_DA3
) * layer1_DA3.output_D_sent_level_rep + transform_gate_DA3 * layer3_DA3.output_D_doc_level_rep
overall_D_A4 = (
1.0 - transform_gate_DA4
) * layer1_DA4.output_D_sent_level_rep + transform_gate_DA4 * layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
overall_A2 = (
1.0 - transform_gate_A2
) * layer1_DA2.output_QA_sent_level_rep + transform_gate_A2 * layer2_A2.output_sent_rep_Dlevel
overall_A3 = (
1.0 - transform_gate_A3
) * layer1_DA3.output_QA_sent_level_rep + transform_gate_A3 * layer2_A3.output_sent_rep_Dlevel
overall_A4 = (
1.0 - transform_gate_A4
) * layer1_DA4.output_QA_sent_level_rep + transform_gate_A4 * layer2_A4.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_sent_level2 = debug_print(
cosine(layer1_DA2.output_D_sent_level_rep,
layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
simi_sent_level3 = debug_print(
cosine(layer1_DA3.output_D_sent_level_rep,
layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
simi_sent_level4 = debug_print(
cosine(layer1_DA4.output_D_sent_level_rep,
layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_doc_level2 = debug_print(
cosine(layer3_DA2.output_D_doc_level_rep,
layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
simi_doc_level3 = debug_print(
cosine(layer3_DA3.output_D_doc_level_rep,
layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
simi_doc_level4 = debug_print(
cosine(layer3_DA4.output_D_doc_level_rep,
layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
simi_overall_level2 = debug_print(cosine(overall_D_A2, overall_A2),
'simi_overall_level2')
simi_overall_level3 = debug_print(cosine(overall_D_A3, overall_A3),
'simi_overall_level3')
simi_overall_level4 = debug_print(cosine(overall_D_A4, overall_A4),
'simi_overall_level4')
simi_1 = simi_overall_level1 #+simi_sent_level1+simi_doc_level1
simi_2 = simi_overall_level2 #+simi_sent_level2+simi_doc_level2
simi_3 = simi_overall_level3 #+simi_sent_level3+simi_doc_level3
simi_4 = simi_overall_level4 #+simi_sent_level4+simi_doc_level4
# simi_1=(simi_overall_level1+simi_sent_level1+simi_doc_level1)/3.0
# simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)+T.maximum(0.0, margin+simi_3-simi_1)+T.maximum(0.0, margin+simi_4-simi_1)
cost12 = T.maximum(
0.0, margin + simi_sent_level2 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level2 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level2 - simi_overall_level1)
cost13 = T.maximum(
0.0, margin + simi_sent_level3 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level3 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level3 - simi_overall_level1)
cost14 = T.maximum(
0.0, margin + simi_sent_level4 - simi_sent_level1) + T.maximum(
0.0, margin + simi_doc_level4 - simi_doc_level1) + T.maximum(
0.0, margin + simi_overall_level4 - simi_overall_level1)
cost = cost12 + cost13 + cost14
posi_simi = T.max([simi_sent_level1, simi_doc_level1, simi_overall_level1])
nega_simi = T.max([
simi_sent_level2, simi_doc_level2, simi_overall_level2,
simi_sent_level3, simi_doc_level3, simi_overall_level3,
simi_sent_level4, simi_doc_level4, simi_overall_level4
])
L2_reg = debug_print(
(high_W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum(), 'L2_reg'
) #+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost = debug_print(cost + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index],
right_A4: test_rightPad_A4[index]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function(
[index],
[cost, posi_simi, nega_simi],
updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index],
[cost, posi_simi, nega_simi],
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index],
right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
corr_train = 0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
((iter % train_size) * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
cost_average, posi_simi, nega_simi = train_model(batch_start)
if posi_simi > nega_simi:
corr_train += 1
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + 'corr rate:' + str(
corr_train * 100.0 / train_size)
if iter % validation_frequency == 0:
corr_test = 0
for i in test_batch_start:
cost, posi_simi, nega_simi = test_model(i)
if posi_simi > nega_simi:
corr_test += 1
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = corr_test * 1.0 / test_size
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better = False
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
print '\t\t\ttest_acc:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
if find_better == True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.085,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=7,
maxSentLength=60,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.00005,
update_freq=10,
norm_threshold=5.0):
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_msr_corpus(rootPath + 'vocab.txt',
rootPath + 'tokenized_train.txt',
rootPath + 'tokenized_test.txt',
maxSentLength)
mtPath = '/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
mt_train, mt_test = load_mts(mtPath + 'concate_15mt_train.txt',
mtPath + 'concate_15mt_test.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int32')
indices_train_r = T.cast(indices_train_r, 'int32')
indices_test_l = T.cast(indices_test_l, 'int32')
indices_test_r = T.cast(indices_test_r, 'int32')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size))
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
cost_tmp = 0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.imatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.imatrix('x_index_r')
y = T.ivector('y')
left_l = T.iscalar()
right_l = T.iscalar()
left_r = T.iscalar()
right_r = T.iscalar()
length_l = T.iscalar()
length_r = T.iscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_size[0],
filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng,
input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_r = Conv_with_input_para(rng,
input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output')
layer0_para = [conv_W, conv_b]
layer1 = Average_Pooling(rng,
input_l=layer0_l_output,
input_r=layer0_r_output,
kern=nkerns[0],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1,
window_size=window_width,
maxSentLength=maxSentLength)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]))
layer2_l = Conv_with_input_para(rng,
input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0],
ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]),
W=conv2_W,
b=conv2_b)
layer2_r = Conv_with_input_para(rng,
input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0],
ishape[1]),
filter_shape=(nkerns[1], 1, nkerns[0],
filter_size[1]),
W=conv2_W,
b=conv2_b)
layer2_para = [conv2_W, conv2_b]
layer3 = Average_Pooling_for_batch1(rng,
input_l=layer2_l.output,
input_r=layer2_r.output,
kern=nkerns[1],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1)
layer3_out = debug_print(layer3.output_simi, 'layer1_out')
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
#norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
#norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
'''
uni_cosine=cosine(sum_uni_l, sum_uni_r)
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer4_input = T.concatenate(
[mts, eucli_1, layer1.output_eucli, layer3_out, len_l, len_r],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4 = LogisticRegression(rng,
input=layer4_input,
n_in=15 + 3 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer4.W**2).sum() + (conv2_W**2).sum() + (conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()
cost_this = debug_print(layer4.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
test_model = theano.function(
[index], [layer4.errors(y), layer4.y_pred],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params + layer2_para + layer0_para # + layer1.params
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
#norm=T.sqrt((grad_i**2).sum())
#if T.lt(norm_threshold, norm):
# print 'big norm'
# grad_i=grad_i*(norm_threshold/norm)
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index], [cost, layer4.errors(y), layer4_input],
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer4.errors(y)],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
if iter % update_freq != 0:
cost_ij, error_ij = train_model_predict(batch_start)
#print 'cost_ij: ', cost_ij
cost_tmp += cost_ij
error_sum += error_ij
else:
cost_average, error_ij, layer3_input = train_model(batch_start)
#print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' sum error: '+str(error_sum)+'/'+str(update_freq)
error_sum = 0
cost_tmp = 0 #reset for the next batch
#print layer3_input
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses = []
for i in test_batch_start:
test_loss, pred_y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
print((
'\t\t\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
#print 'validating & testing...'
# compute zero-one loss on validation set
validation_losses = []
for i in dev_batch_start:
time.sleep(0.5)
validation_losses.append(validate_model(i))
#validation_losses = [validate_model(i) for i in dev_batch_start]
this_validation_loss = numpy.mean(validation_losses)
print('\t\tepoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index , n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [test_model(i) for i in test_batch_start]
test_score = numpy.mean(test_losses)
print(('\t\t\t\tepoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,
test_score * 100.))
'''
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.09,
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=0.06,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=[4, 4],
maxSentLength=64,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.0006,
update_freq=1,
norm_threshold=5.0,
max_truncate=40):
maxSentLength = max_truncate + 2 * (window_width[0] - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/'
rng = numpy.random.RandomState(23455)
datasets, vocab_size = load_wikiQA_corpus(
rootPath + 'vocab.txt', rootPath + 'WikiQA-train.txt',
rootPath + 'test_filtered.txt', max_truncate,
maxSentLength) #vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
mtPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test = load_mts_wikiQA(
mtPath + 'result_train/concate_2mt_train.txt',
mtPath + 'result_test/concate_2mt_test.txt')
wm_train, wm_test = load_wmf_wikiQA(
rootPath + 'train_word_matching_scores.txt',
rootPath + 'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[
0]
indices_train_l = indices_train[::2, :]
indices_train_r = indices_train[1::2, :]
trainLengths_l = trainLengths[::2]
trainLengths_r = trainLengths[1::2]
normalized_train_length_l = normalized_train_length[::2]
normalized_train_length_r = normalized_train_length[1::2]
trainLeftPad_l = trainLeftPad[::2]
trainLeftPad_r = trainLeftPad[1::2]
trainRightPad_l = trainRightPad[::2]
trainRightPad_r = trainRightPad[1::2]
indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[
1]
indices_test_l = indices_test[::2, :]
indices_test_r = indices_test[1::2, :]
testLengths_l = testLengths[::2]
testLengths_r = testLengths[1::2]
normalized_test_length_l = normalized_test_length[::2]
normalized_test_length_r = normalized_test_length[1::2]
testLeftPad_l = testLeftPad[::2]
testLeftPad_r = testLeftPad[1::2]
testRightPad_l = testRightPad[::2]
testRightPad_r = testRightPad[1::2]
n_train_batches = indices_train_l.shape[0] / batch_size
n_test_batches = indices_test_l.shape[0] / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
indices_train_l = theano.shared(numpy.asarray(indices_train_l,
dtype=theano.config.floatX),
borrow=True)
indices_train_r = theano.shared(numpy.asarray(indices_train_r,
dtype=theano.config.floatX),
borrow=True)
indices_test_l = theano.shared(numpy.asarray(indices_test_l,
dtype=theano.config.floatX),
borrow=True)
indices_test_r = theano.shared(numpy.asarray(indices_test_r,
dtype=theano.config.floatX),
borrow=True)
indices_train_l = T.cast(indices_train_l, 'int64')
indices_train_r = T.cast(indices_train_r, 'int64')
indices_test_l = T.cast(indices_test_l, 'int64')
indices_test_r = T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix(
'x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l = T.lscalar()
right_l = T.lscalar()
left_r = T.lscalar()
right_r = T.lscalar()
length_l = T.lscalar()
length_r = T.lscalar()
norm_length_l = T.dscalar()
norm_length_r = T.dscalar()
mts = T.dmatrix()
wmf = T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size = (emb_size, window_width[0])
filter_size_2 = (nkerns[0], window_width[1])
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv = ishape[1] + filter_size[1] - 1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_size[0],
filter_size[1]))
load_model_from_file([conv_W, conv_b])
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng,
input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_r = Conv_with_input_para(rng,
input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0],
ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0],
filter_size[1]),
W=conv_W,
b=conv_b)
layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output')
layer0_para = [conv_W, conv_b]
layer1 = Average_Pooling(rng,
input_l=layer0_l_output,
input_r=layer0_r_output,
kern=nkerns[0],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size[1] - 1,
length_r=length_r + filter_size[1] - 1,
dim=maxSentLength + filter_size[1] - 1,
window_size=window_width[0],
maxSentLength=maxSentLength)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1,
filter_size_2[0],
filter_size_2[1]))
#load_model_from_file([conv2_W, conv2_b])
layer2_l = Conv_with_input_para(
rng,
input=layer1.output_tensor_l,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_r = Conv_with_input_para(
rng,
input=layer1.output_tensor_r,
image_shape=(batch_size, 1, nkerns[0], ishape[1]),
filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]),
W=conv2_W,
b=conv2_b)
layer2_para = [conv2_W, conv2_b]
layer3 = Average_Pooling_for_Top(rng,
input_l=layer2_l.output,
input_r=layer2_r.output,
kern=nkerns[1],
left_l=left_l,
right_l=right_l,
left_r=left_r,
right_r=right_r,
length_l=length_l + filter_size_2[1] - 1,
length_r=length_r + filter_size_2[1] - 1,
dim=maxSentLength + filter_size_2[1] - 1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l = sum_uni_l / layer0_l_input.shape[3]
norm_uni_l = sum_uni_l / T.sqrt((sum_uni_l**2).sum())
sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r = sum_uni_r / layer0_r_input.shape[3]
norm_uni_r = sum_uni_r / T.sqrt((sum_uni_r**2).sum())
uni_cosine = cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine = cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi = debug_print(
T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1, 1)),
'uni_sigmoid_simi')
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l = norm_length_l.reshape((1, 1))
len_r = norm_length_r.reshape((1, 1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input = T.concatenate(
[ #mts,
uni_cosine, #eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
layer1.
output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
layer3.output_cosine,
len_l,
len_r,
wmf
],
axis=1) #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3 = LogisticRegression(rng,
input=layer3_input,
n_in=(1) + (1) + (1) + 2 + 2,
n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer3.W**2).sum() + (conv2_W**2).sum(), 'L2_reg'
) #+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer3.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index], [layer3.prop_for_posi, layer3_input, y],
givens={
x_index_l: indices_test_l[index:index + batch_size],
x_index_r: indices_test_r[index:index + batch_size],
y: testY[index:index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index:index + batch_size],
wmf: wm_test[index:index + batch_size]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params + layer2_para #+layer0_para
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index:index + batch_size],
x_index_r: indices_train_r[index:index + batch_size],
y: trainY[index:index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index:index + batch_size],
wmf: wm_train[index:index + batch_size]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches / 5, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
svm_max = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(
cost_average) + ' error: ' + str(
error_sum) + '/' + str(
update_freq) + ' error rate: ' + str(
error_sum * 1.0 / update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_probs = []
test_y = []
test_features = []
for i in test_batch_start:
prob_i, layer3_input, y = test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_probs.append(prob_i[0][0])
test_y.append(y[0])
test_features.append(layer3_input[0])
MAP, MRR = compute_map_mrr(rootPath + 'test_filtered.txt',
test_probs)
#now, check MAP and MRR
print(
('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best '
'model %f, MRR %f') %
(epoch, minibatch_index, n_train_batches, MAP, MRR))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(C=1.0, kernel='linear')
clf.fit(train_features, train_y)
results_svm = clf.decision_function(test_features)
MAP_svm, MRR_svm = compute_map_mrr(
rootPath + 'test_filtered.txt', results_svm)
lr = LinearRegression().fit(train_features, train_y)
results_lr = lr.predict(test_features)
MAP_lr, MRR_lr = compute_map_mrr(
rootPath + 'test_filtered.txt', results_lr)
print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr
if patience <= iter:
done_looping = True
break
#after each epoch, increase the batch_size
if epoch % 2 == 1:
update_freq = update_freq * 1
else:
update_freq = update_freq / 1
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.08, n_epochs=2000, nkerns=[44], batch_size=1, window_width=3,
maxSentLength=64, emb_size=300, hidden_size=200,
margin=0.5, L2_weight=0.0006, update_freq=1, norm_threshold=5.0, max_truncate=24):
maxSentLength=max_truncate+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/SICK/';
rng = numpy.random.RandomState(23455)
datasets, vocab_size=load_SICK_corpus(rootPath+'vocab_nonoverlap.txt', rootPath+'train_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate,maxSentLength, entailment=True)#vocab_size contain train, dev and test
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
extra_train, extra_test=load_extra_features(rootPath+'train_rule_features_cosine_eucli_negation_len1_len2.txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2.txt')
discri_train, discri_test=load_extra_features(rootPath+'train_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2,:]
indices_train_r=indices_train[1::2,:]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2,:]
indices_test_r=indices_test[1::2,:]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
n_train_batches=indices_train_l.shape[0]/batch_size
n_test_batches=indices_test_l.shape[0]/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
indices_train_l=T.cast(indices_train_l, 'int64')
indices_train_r=T.cast(indices_train_r, 'int64')
indices_test_l=T.cast(indices_test_l, 'int64')
indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_nonoverlap_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer
x_index_r = T.lmatrix('x_index_r')
y = T.lvector('y')
left_l=T.lscalar()
right_l=T.lscalar()
left_r=T.lscalar()
right_r=T.lscalar()
length_l=T.lscalar()
length_r=T.lscalar()
norm_length_l=T.dscalar()
norm_length_r=T.dscalar()
mts=T.dmatrix()
extra=T.dmatrix()
discri=T.dmatrix()
#wmf=T.dmatrix()
cost_tmp=T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
#layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh)
sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size))
aver_uni_l=sum_uni_l/layer0_l_input.shape[3]
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size))
aver_uni_r=sum_uni_r/layer0_r_input.shape[3]
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
aver_uni_cosine=cosine(aver_uni_l, aver_uni_r)
uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi')
linear=Linear(norm_uni_l, norm_uni_r)
poly=Poly(norm_uni_l, norm_uni_r)
sigmoid=Sigmoid(norm_uni_l, norm_uni_r)
rbf=RBF(norm_uni_l, norm_uni_r)
gesd=GESD(norm_uni_l, norm_uni_r)
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r))
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
layer3_input=T.concatenate([mts,
eucli_1,uni_cosine,#linear, poly,sigmoid,rbf, gesd, #sum_uni_r-sum_uni_l,
layer1.output_eucli_to_simi,layer1.output_cosine, #layer1.output_vector_r-layer1.output_vector_l,
len_l, len_r,
extra
#discri
#wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer3=LogisticRegression(rng, input=layer3_input, n_in=14+(2)+(2)+2+5, n_out=3)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [layer3.errors(y),layer3_input, y],
givens={
x_index_l: indices_test_l[index: index + batch_size],
x_index_r: indices_test_r[index: index + batch_size],
y: testY[index: index + batch_size],
left_l: testLeftPad_l[index],
right_l: testRightPad_l[index],
left_r: testLeftPad_r[index],
right_r: testRightPad_r[index],
length_l: testLengths_l[index],
length_r: testLengths_r[index],
norm_length_l: normalized_test_length_l[index],
norm_length_r: normalized_test_length_r[index],
mts: mt_test[index: index + batch_size],
extra: extra_test[index: index + batch_size],
discri:discri_test[index: index + batch_size]
#wmf: wm_test[index: index + batch_size]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params
params_conv = [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([index,cost_tmp], cost, updates=updates,
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y],
givens={
x_index_l: indices_train_l[index: index + batch_size],
x_index_r: indices_train_r[index: index + batch_size],
y: trainY[index: index + batch_size],
left_l: trainLeftPad_l[index],
right_l: trainRightPad_l[index],
left_r: trainLeftPad_r[index],
right_r: trainRightPad_r[index],
length_l: trainLengths_l[index],
length_r: trainLengths_r[index],
norm_length_l: normalized_train_length_l[index],
norm_length_r: normalized_train_length_r[index],
mts: mt_train[index: index + batch_size],
extra: extra_train[index: index + batch_size],
discri:discri_train[index: index + batch_size]
#wmf: wm_train[index: index + batch_size]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
cost_tmp=0.0
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
minibatch_index=minibatch_index+1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter%update_freq != 0:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
#print 'layer3_input', layer3_input
cost_tmp+=cost_ij
error_sum+=error_ij
#print 'cost_acc ',cost_acc
#print 'cost_ij ', cost_ij
#print 'cost_tmp before update',cost_tmp
else:
cost_average= train_model(batch_start,cost_tmp)
#print 'layer3_input', layer3_input
error_sum=0
cost_tmp=0.0#reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq)
#if iter ==1:
# exit(0)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses=[]
test_y=[]
test_features=[]
for i in test_batch_start:
test_loss, layer3_input, y=test_model(i)
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc=1-test_score
print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches,test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y=[]
train_features=[]
count=0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
#lr=LinearRegression().fit(train_features, train_y)
#results_lr=lr.predict(test_features)
corr_count=0
#corr_lr=0
corr_neu=0
neu_co=0
corr_ent=0
ent_co=0
corr_contr=0
contr_co=0
test_size=len(test_y)
for i in range(test_size):
if test_y[i]==0:#NEUTRAL
neu_co+=1
if results[i]==test_y[i]:
corr_neu+=1
elif test_y[i]==1:#ENTAILMENT
ent_co+=1
if results[i]==test_y[i]:
corr_ent+=1
elif test_y[i]==2:#CONTRADICTION
contr_co+=1
if results[i]==test_y[i]:
corr_contr+=1
'''
if results[i]==test_y[i]:
corr_count+=1
if test_y[i]==0: #NEUTRAL
corr_neu+=1
'''
#if numpy.absolute(results_lr[i]-test_y[i])<0.5:
# corr_lr+=1
corr_count=corr_neu+corr_ent+corr_contr
acc=corr_count*1.0/test_size
acc_neu=corr_neu*1.0/neu_co
acc_ent=corr_ent*1.0/ent_co
acc_contr=corr_contr*1.0/contr_co
#acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_epoch=epoch
if test_acc > max_acc:
max_acc=test_acc
best_epoch=epoch
#if acc_lr> max_acc:
# max_acc=acc_lr
# best_epoch=epoch
print '\t\t\tsvm acc: ', acc, ' max acc: ', max_acc,'(at',best_epoch,')',' Neu: ',acc_neu, ' Ent: ',acc_ent, ' Contr: ',acc_contr
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
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]),
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_D_output=debug_print(layer0_D.output, 'layer0_D.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')
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, desT_word_ids_f,
desT_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.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.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[0],
right_l=men_lens[2])
q_rel_pool = Max_Pooling(rng,
input_l=q_rel_conv.output,
left_l=q_word_lens[0],
right_l=q_word_lens[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_desH_pool = Max_Pooling(rng,
input_l=q_desH_conv.output,
left_l=q_word_lens[0],
right_l=q_word_lens[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.output_maxpooling, rel_conv_pool.output_maxpooling)+\
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
def evaluate_lenet5(learning_rate=0.008, n_epochs=2000, nkerns=[400], batch_size=1, window_width=3,
maxSentLength=30, emb_size=300, hidden_size=[300,10],
margin=0.5, L2_weight=0.0001, Div_reg=0.0001, norm_threshold=5.0, use_svm=False):
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/';
rng = numpy.random.RandomState(23455)
datasets, word2id=load_msr_corpus_20161229(rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength)
vocab_size=len(word2id)+1
mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/'
mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt')
wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_number_matching_scores.txt', rootPath+'test_number_matching_scores.txt')
indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0]
indices_train_l=indices_train[::2]
indices_train_r=indices_train[1::2]
trainLengths_l=trainLengths[::2]
trainLengths_r=trainLengths[1::2]
normalized_train_length_l=normalized_train_length[::2]
normalized_train_length_r=normalized_train_length[1::2]
trainLeftPad_l=trainLeftPad[::2]
trainLeftPad_r=trainLeftPad[1::2]
trainRightPad_l=trainRightPad[::2]
trainRightPad_r=trainRightPad[1::2]
indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1]
indices_test_l=indices_test[::2]
indices_test_r=indices_test[1::2]
testLengths_l=testLengths[::2]
testLengths_r=testLengths[1::2]
normalized_test_length_l=normalized_test_length[::2]
normalized_test_length_r=normalized_test_length[1::2]
testLeftPad_l=testLeftPad[::2]
testLeftPad_r=testLeftPad[1::2]
testRightPad_l=testRightPad[::2]
testRightPad_r=testRightPad[1::2]
train_size = len(indices_train_l)
test_size = len(indices_test_l)
train_batch_start=range(train_size)
test_batch_start=range(test_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int32')
# indices_train_r=T.cast(indices_train_r, 'int32')
# indices_test_l=T.cast(indices_test_l, 'int32')
# indices_test_r=T.cast(indices_test_r, 'int32')
rand_values=random_value_normal((vocab_size, emb_size), theano.config.floatX, rng)
# rand_values[0]=numpy.array(numpy.zeros(emb_size))
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init_new(rand_values, id2word, word2vec)
embeddings=theano.shared(value=numpy.array(rand_values,dtype=theano.config.floatX), borrow=True)#theano.shared(value=rand_values, borrow=True)
# allocate symbolic variables for the data
# index = T.iscalar()
x_index_l = T.imatrix() # now, x is the index matrix, must be integer
x_index_r = T.imatrix()
y = T.ivector()
left_l=T.iscalar()
right_l=T.iscalar()
left_r=T.iscalar()
right_r=T.iscalar()
length_l=T.iscalar()
length_r=T.iscalar()
norm_length_l=T.fscalar()
norm_length_r=T.fscalar()
mts=T.fmatrix()
wmf=T.fmatrix()
# cost_tmp=T.fscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # this is the size of MNIST images
filter_size=(emb_size,window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1)
layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]))
conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
#layer0_output = debug_print(layer0.output, 'layer0.output')
layer0_l = Conv_with_input_para(rng, input=layer0_l_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_r = Conv_with_input_para(rng, input=layer0_r_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b)
layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output')
layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output')
layer0_l_output_maxpool = T.max(layer0_l.output_narrow_conv_out[:,:,:,left_l:], axis=3).reshape((1, nkerns[0]))
layer0_r_output_maxpool = T.max(layer0_r.output_narrow_conv_out[:,:,:,left_r:], axis=3).reshape((1, nkerns[0]))
layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0],
left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r,
length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1,
dim=maxSentLength+filter_size[1]-1)
sum_uni_l=T.sum(layer0_l_input[:,:,:,left_l:], axis=3).reshape((1, emb_size))
norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum())
sum_uni_r=T.sum(layer0_r_input[:,:,:,left_r:], axis=3).reshape((1, emb_size))
norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum())
uni_cosine=cosine(sum_uni_l, sum_uni_r)
'''
linear=Linear(sum_uni_l, sum_uni_r)
poly=Poly(sum_uni_l, sum_uni_r)
sigmoid=Sigmoid(sum_uni_l, sum_uni_r)
rbf=RBF(sum_uni_l, sum_uni_r)
gesd=GESD(sum_uni_l, sum_uni_r)
'''
eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2%
#eucli_1=EUCLID(sum_uni_l, sum_uni_r)
len_l=norm_length_l.reshape((1,1))
len_r=norm_length_r.reshape((1,1))
'''
len_l=length_l.reshape((1,1))
len_r=length_r.reshape((1,1))
'''
#length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1))
#length_gap=T.sqrt((len_l-len_r)**2)
#layer3_input=mts
HL_layer_1_input=T.concatenate([
# mts,
eucli_1, #uni_cosine,norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, #
uni_cosine,
# sum_uni_l,
# sum_uni_r,
# sum_uni_l+sum_uni_r,
1.0/(1.0+EUCLID(layer0_l_output_maxpool, layer0_r_output_maxpool)),
cosine(layer0_l_output_maxpool, layer0_r_output_maxpool),
layer0_l_output_maxpool,
layer0_r_output_maxpool,
T.sqrt((layer0_l_output_maxpool-layer0_r_output_maxpool)**2+1e-10),
layer1.output_eucli_to_simi, #layer1.output_cosine,layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, #
layer1.output_cosine,
layer1.output_vector_l,
layer1.output_vector_r,
T.sqrt((layer1.output_vector_l-layer1.output_vector_r)**2+1e-10),
# len_l, len_r
layer1.output_attentions
# wmf,
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
HL_layer_1_input_with_extra=T.concatenate([#HL_layer_1_input,
mts, len_l, len_r
# wmf
], axis=1)#, layer2.output, layer1.output_cosine], axis=1)
HL_layer_1_input_size=1+1+ 1+1+3* nkerns[0] +1+1+3*nkerns[0]+10*10
HL_layer_1_input_with_extra_size = HL_layer_1_input_size+15+2
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size[0], activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size[0], n_out=hidden_size[1], activation=T.tanh)
LR_layer_input=T.concatenate([HL_layer_2.output, HL_layer_1.output, HL_layer_1_input],axis=1)
LR_layer_input_with_extra=T.concatenate([HL_layer_2.output, HL_layer_1_input_with_extra],axis=1)#HL_layer_1.output,
LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=HL_layer_1_input_size+hidden_size[0]+hidden_size[1], n_out=2)
# LR_layer_input=HL_layer_2.output
# LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=hidden_size, n_out=2)
# layer3=LogisticRegression(rng, input=layer3_input, n_in=15+1+1+2+3, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg =debug_print((LR_layer.W** 2).sum()+(HL_layer_2.W** 2).sum()+(HL_layer_1.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()
# diversify_reg= Diversify_Reg(LR_layer.W.T)+Diversify_Reg(HL_layer_2.W.T)+Diversify_Reg(HL_layer_1.W.T)+Diversify_Reg(conv_W_into_matrix)
cost_this =debug_print(LR_layer.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg
cost=cost_this+L2_weight*L2_reg#+Div_reg*diversify_reg
test_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [LR_layer.errors(y), LR_layer.y_pred, LR_layer_input_with_extra, y], on_unused_input='ignore',allow_input_downcast=True)
params = LR_layer.params+ HL_layer_2.params+HL_layer_1.params+[conv_W, conv_b]+[embeddings]#+[embeddings]# + layer1.params
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
clipped_grad = T.clip(grad_i, -0.5, 0.5)
acc = acc_i + T.sqr(clipped_grad)
updates.append((param_i, param_i - learning_rate * clipped_grad / T.sqrt(acc+1e-10))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [cost,LR_layer.errors(y)], updates=updates, on_unused_input='ignore',allow_input_downcast=True)
train_model_predict = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r,
mts,wmf], [cost_this,LR_layer.errors(y), LR_layer_input_with_extra, y],on_unused_input='ignore',allow_input_downcast=True)
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
max_acc=0.0
nn_max_acc=0.0
best_iter=0
cost_tmp=0.0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
for index in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * train_size + minibatch_index +1
minibatch_index=minibatch_index+1
# if iter%update_freq != 0:
# cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start)
# #print 'cost_ij: ', cost_ij
# cost_tmp+=cost_ij
# error_sum+=error_ij
# else:
cost_i, error_i= train_model(indices_train_l[index: index + batch_size],
indices_train_r[index: index + batch_size],
trainY[index: index + batch_size],
trainLeftPad_l[index],
trainRightPad_l[index],
trainLeftPad_r[index],
trainRightPad_r[index],
trainLengths_l[index],
trainLengths_r[index],
normalized_train_length_l[index],
normalized_train_length_r[index],
mt_train[index: index + batch_size],
wm_train[index: index + batch_size])
cost_tmp+=cost_i
if iter < 6000 and iter %100 ==0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter)
if iter >= 6000 and iter % 100 == 0:
# if iter%100 ==0:
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter)
test_losses=[]
test_y=[]
test_features=[]
for index in test_batch_start:
test_loss, pred_y, layer3_input, y=test_model(indices_test_l[index: index + batch_size],
indices_test_r[index: index + batch_size],
testY[index: index + batch_size],
testLeftPad_l[index],
testRightPad_l[index],
testLeftPad_r[index],
testRightPad_r[index],
testLengths_l[index],
testLengths_r[index],
normalized_test_length_l[index],
normalized_test_length_r[index],
mt_test[index: index + batch_size],
wm_test[index: index + batch_size])
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
test_score = numpy.mean(test_losses)
test_acc = (1-test_score) * 100.
if test_acc > nn_max_acc:
nn_max_acc = test_acc
print '\t\t\tepoch:', epoch, 'iter:', iter, 'current acc:', test_acc, 'nn_max_acc:', nn_max_acc
#now, see the results of svm
if use_svm:
train_y=[]
train_features=[]
for index in train_batch_start:
cost_ij, error_ij, layer3_input, y=train_model_predict(indices_train_l[index: index + batch_size],
indices_train_r[index: index + batch_size],
trainY[index: index + batch_size],
trainLeftPad_l[index],
trainRightPad_l[index],
trainLeftPad_r[index],
trainRightPad_r[index],
trainLengths_l[index],
trainLengths_r[index],
normalized_train_length_l[index],
normalized_train_length_r[index],
mt_train[index: index + batch_size],
wm_train[index: index + batch_size])
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(' '.join(map(str,layer3_input[0]))+'\n')
#write_feature.close()
clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results=clf.predict(test_features)
lr=LinearRegression().fit(train_features, train_y)
results_lr=lr.predict(test_features)
corr_count=0
corr_lr=0
test_size=len(test_y)
for i in range(test_size):
if results[i]==test_y[i]:
corr_count+=1
if numpy.absolute(results_lr[i]-test_y[i])<0.5:
corr_lr+=1
acc=corr_count*1.0/test_size
acc_lr=corr_lr*1.0/test_size
if acc > max_acc:
max_acc=acc
best_iter=iter
if acc_lr> max_acc:
max_acc=acc_lr
best_iter=iter
print '\t\t\t\tsvm acc: ', acc, 'LR acc: ', acc_lr, ' max acc: ', max_acc , ' at iter: ', best_iter
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(file_name,
input_filename,
model_filename,
learning_rate=0.001,
n_epochs=2000,
nkerns=[90, 90],
batch_size=1,
window_width=2,
maxSentLength=64,
maxDocLength=60,
emb_size=50,
hidden_size=200,
L2_weight=0.0065,
update_freq=1,
norm_threshold=5.0,
max_s_length=128,
max_d_length=128,
margin=0.3):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
f = open(file_name, 'w')
f.write("model options " + str(model_options) + '\n')
#rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data, _train_Label, train_size, test_data, _test_Label, test_size, vocab_size = load_MCTest_corpus_DPN(
'vocab_table_wenyan.txt', input_filename, input_filename, max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
f.write('train_size : ' + str(train_size))
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[
train_data_D, train_data_A1, train_Label, train_Length_D,
train_Length_D_s, train_Length_A1, train_leftPad_D, train_leftPad_D_s,
train_leftPad_A1, train_rightPad_D, train_rightPad_D_s,
train_rightPad_A1
] = train_data
[
test_data_D, test_data_A1, test_Label, test_Length_D, test_Length_D_s,
test_Length_A1, test_leftPad_D, test_leftPad_D_s, test_leftPad_A1,
test_rightPad_D, test_rightPad_D_s, test_rightPad_A1
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values, 'vectors_wenyan2.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1 = T.lvector()
# index_A2= T.lvector()
# index_A3= T.lvector()
# index_A4= T.lvector()
y = T.lscalar()
len_D = T.lscalar()
len_D_s = T.lvector()
# len_Q=T.lscalar()
len_A1 = T.lscalar()
# len_A2=T.lscalar()
# len_A3=T.lscalar()
# len_A4=T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
# left_Q=T.lscalar()
left_A1 = T.lscalar()
# left_A2=T.lscalar()
# left_A3=T.lscalar()
# left_A4=T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
# right_Q=T.lscalar()
right_A1 = T.lscalar()
# right_A2=T.lscalar()
# right_A3=T.lscalar()
# right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
f.write('... building the model\n')
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#layer0_A2_input = embeddings[index_A2.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A3_input = embeddings[index_A3.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A4_input = embeddings[index_A4.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
layer0_para = [conv_W, conv_b]
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
layer2_para = [conv2_W, conv2_b]
high_W, high_b = create_highw_para(
rng, nkerns[0], nkerns[1]
) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para = [high_W, high_b]
params = layer2_para + layer0_para + highW_para #+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(
rng,
input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
#layer0_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A3 = Conv_with_input_para(rng, input=layer0_A3_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A4 = Conv_with_input_para(rng, input=layer0_A4_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output = debug_print(layer0_A1.output, 'layer0_A1.output')
#layer0_A2_output=debug_print(layer0_A2.output, 'layer0_A2.output')
# layer0_A3_output=debug_print(layer0_A3.output, 'layer0_A3.output')
# layer0_A4_output=debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1 = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A1_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A1,
right_r=right_A1,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A1 + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=1)
#layer1_DA2=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A2_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A2, right_r=right_A2,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A2+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA3=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A3_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A3, right_r=right_A3,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A3+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA4=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A4_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A4, right_r=right_A4,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A4+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(
rng,
input=layer1_DA1.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#layer2_DA2 = Conv_with_input_para(rng, input=layer1_DA2.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA3 = Conv_with_input_para(rng, input=layer1_DA3.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA4 = Conv_with_input_para(rng, input=layer1_DA4.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA1.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#layer2_A2 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA2.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A3 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA3.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A4 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA4.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel = debug_print(
layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
# layer2_A2_output_sent_rep_Dlevel=debug_print(layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
# layer2_A3_output_sent_rep_Dlevel=debug_print(layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
# layer2_A4_output_sent_rep_Dlevel=debug_print(layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1 = Average_Pooling_for_Top(
rng,
input_l=layer2_DA1.output,
input_r=layer2_A1_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=1)
#layer3_DA2=Average_Pooling_for_Top(rng, input_l=layer2_DA2.output, input_r=layer2_A2_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA3=Average_Pooling_for_Top(rng, input_l=layer2_DA3.output, input_r=layer2_A3_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA4=Average_Pooling_for_Top(rng, input_l=layer2_DA4.output, input_r=layer2_A4_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b),
'transform_gate_DA1')
transform_gate_A1 = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b),
'transform_gate_A1')
# transform_gate_A2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b), 'transform_gate_A2')
# transform_gate_A3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b), 'transform_gate_A3')
# transform_gate_A4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b), 'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1 = (
1.0 - transform_gate_DA1
) * layer1_DA1.output_D_sent_level_rep + transform_gate_DA1 * layer3_DA1.output_D_doc_level_rep
# overall_D_A2=(1.0-transform_gate_DA2)*layer1_DA2.output_D_sent_level_rep+transform_gate_DA2*layer3_DA2.output_D_doc_level_rep
# overall_D_A3=(1.0-transform_gate_DA3)*layer1_DA3.output_D_sent_level_rep+transform_gate_DA3*layer3_DA3.output_D_doc_level_rep
# overall_D_A4=(1.0-transform_gate_DA4)*layer1_DA4.output_D_sent_level_rep+transform_gate_DA4*layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1 = (
1.0 - transform_gate_A1
) * layer1_DA1.output_QA_sent_level_rep + transform_gate_A1 * layer2_A1.output_sent_rep_Dlevel
#overall_A2=(1.0-transform_gate_A2)*layer1_DA2.output_QA_sent_level_rep+transform_gate_A2*layer2_A2.output_sent_rep_Dlevel
# overall_A3=(1.0-transform_gate_A3)*layer1_DA3.output_QA_sent_level_rep+transform_gate_A3*layer2_A3.output_sent_rep_Dlevel
# overall_A4=(1.0-transform_gate_A4)*layer1_DA4.output_QA_sent_level_rep+transform_gate_A4*layer2_A4.output_sent_rep_Dlevel
simi_sent_level1 = debug_print(
cosine(layer1_DA1.output_D_sent_level_rep,
layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
#simi_sent_level2=debug_print(cosine(layer1_DA2.output_D_sent_level_rep, layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
# simi_sent_level3=debug_print(cosine(layer1_DA3.output_D_sent_level_rep, layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
# simi_sent_level4=debug_print(cosine(layer1_DA4.output_D_sent_level_rep, layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1 = debug_print(
cosine(layer3_DA1.output_D_doc_level_rep,
layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
#simi_doc_level2=debug_print(cosine(layer3_DA2.output_D_doc_level_rep, layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
# simi_doc_level3=debug_print(cosine(layer3_DA3.output_D_doc_level_rep, layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
# simi_doc_level4=debug_print(cosine(layer3_DA4.output_D_doc_level_rep, layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1 = debug_print(cosine(overall_D_A1, overall_A1),
'simi_overall_level1')
#simi_overall_level2=debug_print(cosine(overall_D_A2, overall_A2), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(overall_D_A3, overall_A3), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(overall_D_A4, overall_A4), 'simi_overall_level4')
# simi_1=simi_overall_level1+simi_sent_level1+simi_doc_level1
# simi_2=simi_overall_level2+simi_sent_level2+simi_doc_level2
simi_1 = (simi_overall_level1 + simi_sent_level1 + simi_doc_level1) / 3.0
#simi_1 = simi_doc_level1
#simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
logistic_w, logistic_b = create_logistic_para(rng, 1, 2)
logistic_para = [logistic_w, logistic_b]
sent_w, sent_b = create_logistic_para(rng, 1, 2)
doc_w, doc_b = create_logistic_para(rng, 1, 2)
sent_para = [sent_w, sent_b]
doc_para = [doc_w, doc_b]
params += logistic_para
params += sent_para
params += doc_para
load_model(params, model_filename)
simi_sent = T.dot(sent_w, simi_sent_level1) + sent_b.dimshuffle(0, 'x')
simi_sent = simi_sent.dimshuffle(1, 0)
simi_sent = T.nnet.softmax(simi_sent)
tmp_sent = T.log(simi_sent)
simi_doc = T.dot(doc_w, simi_doc_level1) + doc_b.dimshuffle(0, 'x')
simi_doc = simi_doc.dimshuffle(1, 0)
simi_doc = T.nnet.softmax(simi_doc)
tmp_doc = T.log(simi_doc)
#cost = margin - simi_1
simi_overall = T.dot(logistic_w,
simi_overall_level1) + logistic_b.dimshuffle(0, 'x')
simi_overall = simi_overall.dimshuffle(1, 0)
simi_overall = T.nnet.softmax(simi_overall)
predict = T.argmax(simi_overall, axis=1)
tmp_overall = T.log(simi_overall)
cost = -(tmp_overall[0][y] + tmp_doc[0][y] + tmp_sent[0][y]) / 3.0
L2_reg = (conv2_W**2).sum() + (conv_W**2).sum() + (logistic_w**2).sum() + (
high_W**2).sum()
cost = cost + L2_weight * L2_reg
#simi_1 = [simi_overall,simi_doc,simi_sent]
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)
#cost=T.maximum(0.0, margin+simi_sent_level2-simi_sent_level1)+T.maximum(0.0, margin+simi_doc_level2-simi_doc_level1)+T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)
# posi_simi=simi_1
# nega_simi=simi_2
#L2_reg =debug_print((high_W**2).sum()+(conv2_W**2).sum()+(conv_W**2).sum(), 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
#cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function(
[index],
[cost, simi_overall, simi_doc, simi_sent, predict],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
y: test_Label[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
# len_A2: test_Length_A2[index],
# len_A3: test_Length_A3[index],
# len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
# left_A2: test_leftPad_A2[index],
# left_A3: test_leftPad_A3[index],
# left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
},
on_unused_input='ignore')
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function(
[index],
[cost, simi_overall, simi_doc, simi_sent, predict],
updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
# index_A2: train_data_A2[index],
# index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
y: train_Label[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
# len_A2: train_Length_A2[index],
# len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
# left_A2: train_leftPad_A2[index],
# left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
# right_A2: train_rightPad_A2[index]
# right_A3: train_rightPad_A3[index],
# right_A4: train_rightPad_A4[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
f.write('... training\n')
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
cost, simi_overall, simi_doc, simi_sent, predict = test_model(0)
cost, simi_overall1, simi_doc, simi_sent, predict = test_model(1)
cost, simi_overall2, simi_doc, simi_sent, predict = test_model(2)
cost, simi_overall3, simi_doc, simi_sent, predict = test_model(3)
return simi_overall, simi_overall1, simi_overall2, simi_overall3
'''
def evaluate_lenet5(learning_rate=0.1, n_epochs=4, L2_weight=0.001, emb_size=70, batch_size=50, filter_size=3, maxSentLen=50, nn='CNN'):
hidden_size=emb_size
model_options = locals().copy()
print "model options", model_options
rng = np.random.RandomState(1234) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r,all_labels, word2id =load_SNLI_dataset(maxlen=maxSentLen) #minlen, include one label, at least one word in the sentence
train_sents_l=np.asarray(all_sentences_l[0], dtype='int32')
dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
test_sents_l=np.asarray(all_sentences_l[2], dtype='int32')
train_masks_l=np.asarray(all_masks_l[0], dtype=theano.config.floatX)
dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
test_masks_l=np.asarray(all_masks_l[2], dtype=theano.config.floatX)
train_sents_r=np.asarray(all_sentences_r[0], dtype='int32')
dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
test_sents_r=np.asarray(all_sentences_r[2] , dtype='int32')
train_masks_r=np.asarray(all_masks_r[0], dtype=theano.config.floatX)
dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
test_masks_r=np.asarray(all_masks_r[2], dtype=theano.config.floatX)
train_labels_store=np.asarray(all_labels[0], dtype='int32')
dev_labels_store=np.asarray(all_labels[1], dtype='int32')
test_labels_store=np.asarray(all_labels[2], dtype='int32')
train_size=len(train_labels_store)
dev_size=len(dev_labels_store)
test_size=len(test_labels_store)
vocab_size=len(word2id)+1
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
# rand_values[0]=np.array(np.zeros(emb_size),dtype=theano.config.floatX)
# id2word = {y:x for x,y in word2id.iteritems()}
# word2vec=load_word2vec()
# rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l=T.imatrix()
sents_mask_l=T.fmatrix()
sents_ids_r=T.imatrix()
sents_mask_r=T.fmatrix()
labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
common_input_l=embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)) #the input format can be adapted into CNN or GRU or LSTM
common_input_r=embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size))
#conv
if nn=='CNN':
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size, 1, emb_size, filter_size))
conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3]))
NN_para=[conv_W, conv_b]
conv_input_l = common_input_l.dimshuffle((0,'x', 2,1)) #(batch_size, 1, emb_size, maxsenlen)
conv_model_l = Conv_with_input_para(rng, input=conv_input_l,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size), W=conv_W, b=conv_b)
conv_output_l=conv_model_l.narrow_conv_out #(batch, 1, hidden_size, maxsenlen-filter_size+1)
conv_output_into_tensor3_l=conv_output_l.reshape((batch_size, hidden_size, maxSentLen-filter_size+1))
mask_for_conv_output_l=T.repeat(sents_mask_l[:,filter_size-1:].reshape((batch_size, 1, maxSentLen-filter_size+1)), hidden_size, axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_l=(1.0-mask_for_conv_output_l)*(mask_for_conv_output_l-10)
masked_conv_output_l=conv_output_into_tensor3_l+mask_for_conv_output_l #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings_l=T.max(masked_conv_output_l, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
conv_input_r = common_input_r.dimshuffle((0,'x', 2,1)) #(batch_size, 1, emb_size, maxsenlen)
conv_model_r = Conv_with_input_para(rng, input=conv_input_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size, 1, emb_size, filter_size), W=conv_W, b=conv_b)
conv_output_r=conv_model_r.narrow_conv_out #(batch, 1, hidden_size, maxsenlen-filter_size+1)
conv_output_into_tensor3_r=conv_output_r.reshape((batch_size, hidden_size, maxSentLen-filter_size+1))
mask_for_conv_output_r=T.repeat(sents_mask_r[:,filter_size-1:].reshape((batch_size, 1, maxSentLen-filter_size+1)), hidden_size, axis=1) #(batch_size, emb_size, maxSentLen-filter_size+1)
mask_for_conv_output_r=(1.0-mask_for_conv_output_r)*(mask_for_conv_output_r-10)
masked_conv_output_r=conv_output_into_tensor3_r+mask_for_conv_output_r #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings_r=T.max(masked_conv_output_r, axis=2) #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
#GRU
if nn=='GRU':
U1, W1, b1=create_GRU_para(rng, emb_size, hidden_size)
NN_para=[U1, W1, b1] #U1 includes 3 matrices, W1 also includes 3 matrices b1 is bias
gru_input_l = common_input_l.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer_l=GRU_Batch_Tensor_Input_with_Mask(gru_input_l, sents_mask_l, hidden_size, U1, W1, b1)
sent_embeddings_l=gru_layer_l.output_sent_rep # (batch_size, hidden_size)
gru_input_r = common_input_r.dimshuffle((0,2,1)) #gru requires input (batch_size, emb_size, maxSentLen)
gru_layer_r=GRU_Batch_Tensor_Input_with_Mask(gru_input_r, sents_mask_r, hidden_size, U1, W1, b1)
sent_embeddings_r=gru_layer_r.output_sent_rep # (batch_size, hidden_size)
#LSTM
if nn=='LSTM':
LSTM_para_dict=create_LSTM_para(rng, emb_size, hidden_size)
NN_para=LSTM_para_dict.values() # .values returns a list of parameters
lstm_input_l = common_input_l.dimshuffle((0,2,1)) #LSTM has the same inpur format with GRU
lstm_layer_l=LSTM_Batch_Tensor_Input_with_Mask(lstm_input_l, sents_mask_l, hidden_size, LSTM_para_dict)
sent_embeddings_l=lstm_layer_l.output_sent_rep # (batch_size, hidden_size)
lstm_input_r = common_input_r.dimshuffle((0,2,1)) #LSTM has the same inpur format with GRU
lstm_layer_r=LSTM_Batch_Tensor_Input_with_Mask(lstm_input_r, sents_mask_r, hidden_size, LSTM_para_dict)
sent_embeddings_r=lstm_layer_r.output_sent_rep # (batch_size, hidden_size)
HL_layer_1_input = T.concatenate([sent_embeddings_l,sent_embeddings_r, sent_embeddings_l*sent_embeddings_r, cosine_matrix1_matrix2_rowwise(sent_embeddings_l,sent_embeddings_r).dimshuffle(0,'x')],axis=1)
HL_layer_1_input_size = hidden_size*3+1
HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size, activation=T.tanh)
HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size, n_out=hidden_size, activation=T.tanh)
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
LR_input_size=HL_layer_1_input_size+2*hidden_size
U_a = create_ensemble_para(rng, 3, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a, LR_b]
LR_input=T.concatenate([HL_layer_1_input, HL_layer_1.output, HL_layer_2.output],axis=1)
layer_LR=LogisticRegression(rng, input=T.tanh(LR_input), n_in=LR_input_size, n_out=3, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
loss=layer_LR.negative_log_likelihood(labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
params = [embeddings]+NN_para+LR_para+HL_layer_1.params+HL_layer_2.params # put all model parameters together
# L2_reg =L2norm_paraList([embeddings,conv_W, U_a])
# diversify_reg= Diversify_Reg(U_a.T)+Diversify_Reg(conv_W_into_matrix)
cost=loss#+Div_reg*diversify_reg#+L2_weight*L2_reg
grads = T.grad(cost, params) # create a list of gradients for all model parameters
accumulator=[]
for para_i in params:
eps_p=np.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / (T.sqrt(acc)+1e-8))) #1e-8 is add to get rid of zero division
updates.append((acc_i, acc))
#train_model = theano.function([sents_id_matrix, sents_mask, labels], cost, updates=updates, on_unused_input='ignore')
train_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
n_train_batches=train_size/batch_size
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_dev_batches=dev_size/batch_size
dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches=test_size/batch_size
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
max_acc_dev=0.0
max_acc_test=0.0
while epoch < n_epochs:
epoch = epoch + 1
train_indices = range(train_size)
random.Random(200).shuffle(train_indices) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu=0
cost_i=0.0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu +1
iter_accu+=1
train_id_batch = train_indices[batch_id:batch_id+batch_size]
cost_i+= train_model(
train_sents_l[train_id_batch],
train_masks_l[train_id_batch],
train_sents_r[train_id_batch],
train_masks_r[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter%500==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
# if epoch >=3 and iter >= len(train_batch_start)*2.0/3 and iter%500==0:
# print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
# past_time = time.time()
error_sum=0.0
for dev_batch_id in dev_batch_start: # for each test batch
error_i=dev_model(
dev_sents_l[dev_batch_id:dev_batch_id+batch_size],
dev_masks_l[dev_batch_id:dev_batch_id+batch_size],
dev_sents_r[dev_batch_id:dev_batch_id+batch_size],
dev_masks_r[dev_batch_id:dev_batch_id+batch_size],
dev_labels_store[dev_batch_id:dev_batch_id+batch_size]
)
error_sum+=error_i
dev_accuracy=1.0-error_sum/(len(dev_batch_start))
if dev_accuracy > max_acc_dev:
max_acc_dev=dev_accuracy
print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
#best dev model, do test
error_sum=0.0
for test_batch_id in test_batch_start: # for each test batch
error_i=test_model(
test_sents_l[test_batch_id:test_batch_id+batch_size],
test_masks_l[test_batch_id:test_batch_id+batch_size],
test_sents_r[test_batch_id:test_batch_id+batch_size],
test_masks_r[test_batch_id:test_batch_id+batch_size],
test_labels_store[test_batch_id:test_batch_id+batch_size]
)
error_sum+=error_i
test_accuracy=1.0-error_sum/(len(test_batch_start))
if test_accuracy > max_acc_test:
max_acc_test=test_accuracy
print '\t\tcurrent testbacc:', test_accuracy, '\t\t\t\t\tmax_acc_test:', max_acc_test
else:
print 'current dev_accuracy:', dev_accuracy, '\t\t\t\t\tmax max_acc_dev:', max_acc_dev
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 >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
