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(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=0.001, n_epochs=2000, nkerns=[90,90], batch_size=1, window_width=2,
maxSentLength=64, maxDocLength=60, emb_size=50, hidden_size=200,
L2_weight=0.0065, update_freq=1, norm_threshold=5.0, max_s_length=57, max_d_length=59, margin=0.2):
maxSentLength=max_s_length+2*(window_width-1)
maxDocLength=max_d_length+2*(window_width-1)
model_options = locals().copy()
print "model options", model_options
rootPath='/mounts/data/proj/wenpeng/Dataset/MCTest/';
rng = numpy.random.RandomState(23455)
train_data,train_size, test_data, test_size, vocab_size=load_MCTest_corpus_DPNQ(rootPath+'vocab_DPNQ.txt', rootPath+'mc500.train.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt', rootPath+'mc500.test.tsv_standardlized.txt_with_state.txt_DSSSS.txt_DPN.txt_DPNQ.txt', max_s_length,maxSentLength, maxDocLength)#vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
# results=[numpy.array(data_D), numpy.array(data_Q), numpy.array(data_A1), numpy.array(data_A2), numpy.array(data_A3), numpy.array(data_A4), numpy.array(Label),
# numpy.array(Length_D),numpy.array(Length_D_s), numpy.array(Length_Q), numpy.array(Length_A1), numpy.array(Length_A2), numpy.array(Length_A3), numpy.array(Length_A4),
# numpy.array(leftPad_D),numpy.array(leftPad_D_s), numpy.array(leftPad_Q), numpy.array(leftPad_A1), numpy.array(leftPad_A2), numpy.array(leftPad_A3), numpy.array(leftPad_A4),
# numpy.array(rightPad_D),numpy.array(rightPad_D_s), numpy.array(rightPad_Q), numpy.array(rightPad_A1), numpy.array(rightPad_A2), numpy.array(rightPad_A3), numpy.array(rightPad_A4)]
# return results, line_control
[train_data_D, train_data_A1, train_data_A2, train_data_A3, train_Label,
train_Length_D,train_Length_D_s, train_Length_A1, train_Length_A2, train_Length_A3,
train_leftPad_D,train_leftPad_D_s, train_leftPad_A1, train_leftPad_A2, train_leftPad_A3,
train_rightPad_D,train_rightPad_D_s, train_rightPad_A1, train_rightPad_A2, train_rightPad_A3]=train_data
[test_data_D, test_data_A1, test_data_A2, test_data_A3, test_Label,
test_Length_D,test_Length_D_s, test_Length_A1, test_Length_A2, test_Length_A3,
test_leftPad_D,test_leftPad_D_s, test_leftPad_A1, test_leftPad_A2, test_leftPad_A3,
test_rightPad_D,test_rightPad_D_s, test_rightPad_A1, test_rightPad_A2, test_rightPad_A3]=test_data
n_train_batches=train_size/batch_size
n_test_batches=test_size/batch_size
train_batch_start=list(numpy.arange(n_train_batches)*batch_size)
test_batch_start=list(numpy.arange(n_test_batches)*batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234))
rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_DPNQ_glove_50d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings=theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum=0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
index_A1= T.lvector()
index_A2= T.lvector()
index_A3= T.lvector()
# index_A4= T.lvector()
# y = T.lvector()
len_D=T.lscalar()
len_D_s=T.lvector()
# len_Q=T.lscalar()
len_A1=T.lscalar()
len_A2=T.lscalar()
len_A3=T.lscalar()
# len_A4=T.lscalar()
left_D=T.lscalar()
left_D_s=T.lvector()
# left_Q=T.lscalar()
left_A1=T.lscalar()
left_A2=T.lscalar()
left_A3=T.lscalar()
# left_A4=T.lscalar()
right_D=T.lscalar()
right_D_s=T.lvector()
# right_Q=T.lscalar()
right_A1=T.lscalar()
right_A2=T.lscalar()
right_A3=T.lscalar()
# right_A4=T.lscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words=(emb_size,window_width)
filter_sents=(nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_Q_input = embeddings[index_Q.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# layer0_A4_input = embeddings[index_A4.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]))
layer0_para=[conv_W, conv_b]
conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
layer2_para=[conv2_W, conv2_b]
high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1]) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para=[high_W, high_b]
params = layer2_para+layer0_para+highW_para#+[embeddings]
#load_model(params)
layer0_D = Conv_with_input_para(rng, input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_Q = Conv_with_input_para(rng, input=layer0_Q_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(rng, input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A3 = Conv_with_input_para(rng, input=layer0_A3_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
# layer0_A4 = Conv_with_input_para(rng, input=layer0_A4_input,
# image_shape=(batch_size, 1, ishape[0], ishape[1]),
# filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_D_output=debug_print(layer0_D.output, 'layer0_D.output')
# layer0_Q_output=debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A1_output=debug_print(layer0_A1.output, 'layer0_A1.output')
layer0_A2_output=debug_print(layer0_A2.output, 'layer0_A2.output')
layer0_A3_output=debug_print(layer0_A3.output, 'layer0_A3.output')
# layer0_A4_output=debug_print(layer0_A4.output, 'layer0_A4.output')
# layer1_DQ=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_Q_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_Q, right_r=right_Q,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_Q+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA1=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A1_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A1, right_r=right_A1,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A1+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA2=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A2_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A2, right_r=right_A2,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A2+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
layer1_DA3=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A3_output, kern=nkerns[0],
left_D=left_D, right_D=right_D,
left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A3, right_r=right_A3,
length_D_s=len_D_s+filter_words[1]-1, length_r=len_A3+filter_words[1]-1,
dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
# layer1_DA4=Average_Pooling_Scan(rng, input_D=layer0_D_output, input_r=layer0_A4_output, kern=nkerns[0],
# left_D=left_D, right_D=right_D,
# left_D_s=left_D_s, right_D_s=right_D_s, left_r=left_A4, right_r=right_A4,
# length_D_s=len_D_s+filter_words[1]-1, length_r=len_A4+filter_words[1]-1,
# dim=maxSentLength+filter_words[1]-1, doc_len=maxDocLength, topk=3)
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
# layer2_DQ = Conv_with_input_para(rng, input=layer1_DQ.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA1 = Conv_with_input_para(rng, input=layer1_DA1.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA2 = Conv_with_input_para(rng, input=layer1_DA2.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_DA3 = Conv_with_input_para(rng, input=layer1_DA3.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_DA4 = Conv_with_input_para(rng, input=layer1_DA4.output_D.reshape((batch_size, 1, nkerns[0], dshape[1])),
# image_shape=(batch_size, 1, nkerns[0], dshape[1]),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
#conv single Q and A into doc level with same conv weights
# layer2_Q = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DQ.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A1 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA1.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A2 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA2.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
layer2_A3 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA3.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_A4 = Conv_with_input_para_one_col_featuremap(rng, input=layer1_DA4.output_QA_sent_level_rep.reshape((batch_size, 1, nkerns[0], 1)),
# image_shape=(batch_size, 1, nkerns[0], 1),
# filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]), W=conv2_W, b=conv2_b)
# layer2_Q_output_sent_rep_Dlevel=debug_print(layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A1_output_sent_rep_Dlevel=debug_print(layer2_A1.output_sent_rep_Dlevel, 'layer2_A1.output_sent_rep_Dlevel')
layer2_A2_output_sent_rep_Dlevel=debug_print(layer2_A2.output_sent_rep_Dlevel, 'layer2_A2.output_sent_rep_Dlevel')
layer2_A3_output_sent_rep_Dlevel=debug_print(layer2_A3.output_sent_rep_Dlevel, 'layer2_A3.output_sent_rep_Dlevel')
# layer2_A4_output_sent_rep_Dlevel=debug_print(layer2_A4.output_sent_rep_Dlevel, 'layer2_A4.output_sent_rep_Dlevel')
# layer3_DQ=Average_Pooling_for_Top(rng, input_l=layer2_DQ.output, input_r=layer2_Q_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA1=Average_Pooling_for_Top(rng, input_l=layer2_DA1.output, input_r=layer2_A1_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA2=Average_Pooling_for_Top(rng, input_l=layer2_DA2.output, input_r=layer2_A2_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
layer3_DA3=Average_Pooling_for_Top(rng, input_l=layer2_DA3.output, input_r=layer2_A3_output_sent_rep_Dlevel, kern=nkerns[1],
left_l=left_D, right_l=right_D, left_r=0, right_r=0,
length_l=len_D+filter_sents[1]-1, length_r=1,
dim=maxDocLength+filter_sents[1]-1, topk=3)
# layer3_DA4=Average_Pooling_for_Top(rng, input_l=layer2_DA4.output, input_r=layer2_A4_output_sent_rep_Dlevel, kern=nkerns[1],
# left_l=left_D, right_l=right_D, left_r=0, right_r=0,
# length_l=len_D+filter_sents[1]-1, length_r=1,
# dim=maxDocLength+filter_sents[1]-1, topk=3)
#high-way
# transform_gate_DQ=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b), 'transform_gate_DQ')
transform_gate_DA1=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA1.output_D_sent_level_rep) + high_b), 'transform_gate_DA1')
transform_gate_DA2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_D_sent_level_rep) + high_b), 'transform_gate_DA2')
transform_gate_DA3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_D_sent_level_rep) + high_b), 'transform_gate_DA3')
# transform_gate_DA4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_D_sent_level_rep) + high_b), 'transform_gate_DA4')
# transform_gate_Q=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b), 'transform_gate_Q')
transform_gate_A1=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA1.output_QA_sent_level_rep) + high_b), 'transform_gate_A1')
transform_gate_A2=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA2.output_QA_sent_level_rep) + high_b), 'transform_gate_A2')
# transform_gate_A3=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA3.output_QA_sent_level_rep) + high_b), 'transform_gate_A3')
# transform_gate_A4=debug_print(T.nnet.sigmoid(T.dot(high_W, layer1_DA4.output_QA_sent_level_rep) + high_b), 'transform_gate_A4')
# overall_D_Q=debug_print((1.0-transform_gate_DQ)*layer1_DQ.output_D_sent_level_rep+transform_gate_DQ*layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A1=(1.0-transform_gate_DA1)*layer1_DA1.output_D_sent_level_rep+transform_gate_DA1*layer3_DA1.output_D_doc_level_rep
overall_D_A2=(1.0-transform_gate_DA2)*layer1_DA2.output_D_sent_level_rep+transform_gate_DA2*layer3_DA2.output_D_doc_level_rep
overall_D_A3=(1.0-transform_gate_DA3)*layer1_DA3.output_D_sent_level_rep+transform_gate_DA3*layer3_DA3.output_D_doc_level_rep
# overall_D_A4=(1.0-transform_gate_DA4)*layer1_DA4.output_D_sent_level_rep+transform_gate_DA4*layer3_DA4.output_D_doc_level_rep
# overall_Q=(1.0-transform_gate_Q)*layer1_DQ.output_QA_sent_level_rep+transform_gate_Q*layer2_Q.output_sent_rep_Dlevel
overall_A1=(1.0-transform_gate_A1)*layer1_DA1.output_QA_sent_level_rep+transform_gate_A1*layer2_A1.output_sent_rep_Dlevel
overall_A2=(1.0-transform_gate_A2)*layer1_DA2.output_QA_sent_level_rep+transform_gate_A2*layer2_A2.output_sent_rep_Dlevel
# overall_A3=(1.0-transform_gate_A3)*layer1_DA3.output_QA_sent_level_rep+transform_gate_A3*layer2_A3.output_sent_rep_Dlevel
# overall_A4=(1.0-transform_gate_A4)*layer1_DA4.output_QA_sent_level_rep+transform_gate_A4*layer2_A4.output_sent_rep_Dlevel
simi_sent_level1=debug_print(cosine(layer1_DA1.output_D_sent_level_rep, layer1_DA1.output_QA_sent_level_rep), 'simi_sent_level1')
simi_sent_level2=debug_print(cosine(layer1_DA2.output_D_sent_level_rep, layer1_DA2.output_QA_sent_level_rep), 'simi_sent_level2')
# simi_sent_level3=debug_print(cosine(layer1_DA3.output_D_sent_level_rep, layer1_DA3.output_QA_sent_level_rep), 'simi_sent_level3')
# simi_sent_level4=debug_print(cosine(layer1_DA4.output_D_sent_level_rep, layer1_DA4.output_QA_sent_level_rep), 'simi_sent_level4')
simi_doc_level1=debug_print(cosine(layer3_DA1.output_D_doc_level_rep, layer2_A1.output_sent_rep_Dlevel), 'simi_doc_level1')
simi_doc_level2=debug_print(cosine(layer3_DA2.output_D_doc_level_rep, layer2_A2.output_sent_rep_Dlevel), 'simi_doc_level2')
# simi_doc_level3=debug_print(cosine(layer3_DA3.output_D_doc_level_rep, layer2_A3.output_sent_rep_Dlevel), 'simi_doc_level3')
# simi_doc_level4=debug_print(cosine(layer3_DA4.output_D_doc_level_rep, layer2_A4.output_sent_rep_Dlevel), 'simi_doc_level4')
simi_overall_level1=debug_print(cosine(overall_D_A1, overall_A1), 'simi_overall_level1')
simi_overall_level2=debug_print(cosine(overall_D_A2, overall_A2), 'simi_overall_level2')
# simi_overall_level3=debug_print(cosine(overall_D_A3, overall_A3), 'simi_overall_level3')
# simi_overall_level4=debug_print(cosine(overall_D_A4, overall_A4), 'simi_overall_level4')
# simi_1=simi_overall_level1+simi_sent_level1+simi_doc_level1
# simi_2=simi_overall_level2+simi_sent_level2+simi_doc_level2
simi_1=(simi_overall_level1+simi_sent_level1+simi_doc_level1)/3.0
simi_2=(simi_overall_level2+simi_sent_level2+simi_doc_level2)/3.0
# simi_3=(simi_overall_level3+simi_sent_level3+simi_doc_level3)/3.0
# simi_4=(simi_overall_level4+simi_sent_level4+simi_doc_level4)/3.0
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
# #only use overall_simi
# cost=T.maximum(0.0, margin+T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])-simi_overall_level1) # ranking loss: max(0, margin-nega+posi)
# posi_simi=simi_overall_level1
# nega_simi=T.max([simi_overall_level2, simi_overall_level3, simi_overall_level4])
#use ensembled simi
# cost=T.maximum(0.0, margin+T.max([simi_2, simi_3, simi_4])-simi_1) # ranking loss: max(0, margin-nega+posi)
# cost=T.maximum(0.0, margin+simi_2-simi_1)
simi_PQ=cosine(layer1_DA1.output_QA_sent_level_rep, layer1_DA3.output_D_sent_level_rep)
simi_NQ=cosine(layer1_DA2.output_QA_sent_level_rep, layer1_DA3.output_D_sent_level_rep)
#bad matching at overall level
# simi_PQ=cosine(overall_A1, overall_D_A3)
# simi_NQ=cosine(overall_A2, overall_D_A3)
match_cost=T.maximum(0.0, margin+simi_NQ-simi_PQ)
cost=T.maximum(0.0, margin+simi_sent_level2-simi_sent_level1)+T.maximum(0.0, margin+simi_doc_level2-simi_doc_level1)+T.maximum(0.0, margin+simi_overall_level2-simi_overall_level1)
cost=cost#+match_cost
# posi_simi=simi_1
# nega_simi=simi_2
L2_reg =debug_print((high_W**2).sum()+3*(conv2_W**2).sum()+(conv_W**2).sum(), 'L2_reg')#+(embeddings**2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum()
cost=debug_print(cost+L2_weight*L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
test_model = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2],
givens={
index_D: test_data_D[index], #a matrix
# index_Q: test_data_Q[index],
index_A1: test_data_A1[index],
index_A2: test_data_A2[index],
index_A3: test_data_A3[index],
# index_A4: test_data_A4[index],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
# len_Q: test_Length_Q[index],
len_A1: test_Length_A1[index],
len_A2: test_Length_A2[index],
len_A3: test_Length_A3[index],
# len_A4: test_Length_A4[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
# left_Q: test_leftPad_Q[index],
left_A1: test_leftPad_A1[index],
left_A2: test_leftPad_A2[index],
left_A3: test_leftPad_A3[index],
# left_A4: test_leftPad_A4[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
# right_Q: test_rightPad_Q[index],
right_A1: test_rightPad_A1[index],
right_A2: test_rightPad_A2[index],
right_A3: test_rightPad_A3[index]
# right_A4: test_rightPad_A4[index]
}, on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
accumulator=[]
for para_i in params:
eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i=debug_print(grad_i,'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
# for param_i, grad_i, acc_i in zip(params, grads, accumulator):
# acc = acc_i + T.sqr(grad_i)
# if param_i == embeddings:
# updates.append((param_i, T.set_subtensor((param_i - learning_rate * grad_i / T.sqrt(acc))[0], theano.shared(numpy.zeros(emb_size))))) #AdaGrad
# else:
# updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
# updates.append((acc_i, acc))
train_model = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2], updates=updates,
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index]
# right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
train_model_predict = theano.function([index], [cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2],
givens={
index_D: train_data_D[index],
# index_Q: train_data_Q[index],
index_A1: train_data_A1[index],
index_A2: train_data_A2[index],
index_A3: train_data_A3[index],
# index_A4: train_data_A4[index],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
# len_Q: train_Length_Q[index],
len_A1: train_Length_A1[index],
len_A2: train_Length_A2[index],
len_A3: train_Length_A3[index],
# len_A4: train_Length_A4[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
# left_Q: train_leftPad_Q[index],
left_A1: train_leftPad_A1[index],
left_A2: train_leftPad_A2[index],
left_A3: train_leftPad_A3[index],
# left_A4: train_leftPad_A4[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
# right_Q: train_rightPad_Q[index],
right_A1: train_rightPad_A1[index],
right_A2: train_rightPad_A2[index],
right_A3: train_rightPad_A3[index]
# right_A4: train_rightPad_A4[index]
}, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc=0.0
best_epoch=0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index=0
shuffle(train_batch_start)#shuffle training data
posi_train_sent=[]
nega_train_sent=[]
posi_train_doc=[]
nega_train_doc=[]
posi_train_overall=[]
nega_train_overall=[]
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index +1
sys.stdout.write( "Training :[%6f] %% complete!\r" % ((iter%train_size)*100.0/train_size) )
sys.stdout.flush()
minibatch_index=minibatch_index+1
cost_average, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2= train_model(batch_start)
posi_train_sent.append(simi_sent_level1)
nega_train_sent.append(simi_sent_level2)
posi_train_doc.append(simi_doc_level1)
nega_train_doc.append(simi_doc_level2)
posi_train_overall.append(simi_overall_level1)
nega_train_overall.append(simi_overall_level2)
if iter % n_train_batches == 0:
corr_train_sent=compute_corr(posi_train_sent, nega_train_sent)
corr_train_doc=compute_corr(posi_train_doc, nega_train_doc)
corr_train_overall=compute_corr(posi_train_overall, nega_train_overall)
print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+'corr rate:'+str(corr_train_sent*300.0/train_size)+' '+str(corr_train_doc*300.0/train_size)+' '+str(corr_train_overall*300.0/train_size)
if iter % validation_frequency == 0:
posi_test_sent=[]
nega_test_sent=[]
posi_test_doc=[]
nega_test_doc=[]
posi_test_overall=[]
nega_test_overall=[]
for i in test_batch_start:
cost, simi_sent_level1, simi_sent_level2, simi_doc_level1, simi_doc_level2, simi_overall_level1, simi_overall_level2=test_model(i)
posi_test_sent.append(simi_sent_level1)
nega_test_sent.append(simi_sent_level2)
posi_test_doc.append(simi_doc_level1)
nega_test_doc.append(simi_doc_level2)
posi_test_overall.append(simi_overall_level1)
nega_test_overall.append(simi_overall_level2)
corr_test_sent=compute_corr(posi_test_sent, nega_test_sent)
corr_test_doc=compute_corr(posi_test_doc, nega_test_doc)
corr_test_overall=compute_corr(posi_test_overall, nega_test_overall)
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc_sent=corr_test_sent*1.0/(test_size/3.0)
test_acc_doc=corr_test_doc*1.0/(test_size/3.0)
test_acc_overall=corr_test_overall*1.0/(test_size/3.0)
#test_acc=1-test_score
# print(('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
# 'model %f %%') %
# (epoch, minibatch_index, n_train_batches,test_acc * 100.))
print '\t\t\tepoch', epoch, ', minibatch', minibatch_index, '/', n_train_batches, 'test acc of best model', test_acc_sent*100,test_acc_doc*100,test_acc_overall*100
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
find_better=False
if test_acc_sent > max_acc:
max_acc=test_acc_sent
best_epoch=epoch
find_better=True
if test_acc_doc > max_acc:
max_acc=test_acc_doc
best_epoch=epoch
find_better=True
if test_acc_overall > max_acc:
max_acc=test_acc_overall
best_epoch=epoch
find_better=True
print '\t\t\tmax:', max_acc,'(at',best_epoch,')'
if find_better==True:
store_model_to_file(params, best_epoch, max_acc)
print 'Finished storing best params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock()-mid_time)/60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.09,
n_epochs=2000,
nkerns=[50, 50],
batch_size=1,
window_width=3,
maxSentLength=64,
maxDocLength=60,
emb_size=300,
hidden_size=200,
margin=0.5,
L2_weight=0.00065,
update_freq=1,
norm_threshold=5.0,
max_s_length=57,
max_d_length=59):
maxSentLength = max_s_length + 2 * (window_width - 1)
maxDocLength = max_d_length + 2 * (window_width - 1)
model_options = locals().copy()
print "model options", model_options
rootPath = '/mounts/data/proj/wenpeng/Dataset/MCTest/'
rng = numpy.random.RandomState(23455)
train_data, train_size, test_data, test_size, vocab_size = load_MCTest_corpus(
rootPath + 'vocab.txt', rootPath + 'mc500.train.tsv_standardlized.txt',
rootPath + 'mc500.test.tsv_standardlized.txt', max_s_length,
maxSentLength, maxDocLength) #vocab_size contain train, dev and test
#datasets_nonoverlap, vocab_size_nonoverlap=load_SICK_corpus(rootPath+'vocab_nonoverlap_train_plus_dev.txt', rootPath+'train_plus_dev_removed_overlap_as_training.txt', rootPath+'test_removed_overlap_as_training.txt', max_truncate_nonoverlap,maxSentLength_nonoverlap, entailment=True)
#datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test
#mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/'
# mt_train, mt_test=load_mts_wikiQA(rootPath+'Train_plus_dev_MT/concate_14mt_train.txt', rootPath+'Test_MT/concate_14mt_test.txt')
# extra_train, extra_test=load_extra_features(rootPath+'train_plus_dev_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt', rootPath+'test_rule_features_cosine_eucli_negation_len1_len2_syn_hyper1_hyper2_anto(newsimi0.4).txt')
# discri_train, discri_test=load_extra_features(rootPath+'train_plus_dev_discri_features_0.3.txt', rootPath+'test_discri_features_0.3.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt')
#wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt')
[
train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
train_Length_D, train_Length_D_s, train_Length_Q, train_Length_A,
train_leftPad_D, train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
train_rightPad_D, train_rightPad_D_s, train_rightPad_Q,
train_rightPad_A
] = train_data
[
test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
test_Length_D, test_Length_D_s, test_Length_Q, test_Length_A,
test_leftPad_D, test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
test_rightPad_D, test_rightPad_D_s, test_rightPad_Q, test_rightPad_A
] = test_data
n_train_batches = train_size / batch_size
n_test_batches = test_size / batch_size
train_batch_start = list(numpy.arange(n_train_batches) * batch_size)
test_batch_start = list(numpy.arange(n_test_batches) * batch_size)
# indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True)
# indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True)
# indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True)
# indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True)
# indices_train_l=T.cast(indices_train_l, 'int64')
# indices_train_r=T.cast(indices_train_r, 'int64')
# indices_test_l=T.cast(indices_test_l, 'int64')
# indices_test_r=T.cast(indices_test_r, 'int64')
rand_values = random_value_normal((vocab_size + 1, emb_size),
theano.config.floatX,
numpy.random.RandomState(1234))
rand_values[0] = numpy.array(numpy.zeros(emb_size),
dtype=theano.config.floatX)
#rand_values[0]=numpy.array([1e-50]*emb_size)
rand_values = load_word2vec_to_init(rand_values,
rootPath + 'vocab_embs_300d.txt')
#rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt')
embeddings = theano.shared(value=rand_values, borrow=True)
#cost_tmp=0
error_sum = 0
# allocate symbolic variables for the data
index = T.lscalar()
index_D = T.lmatrix() # now, x is the index matrix, must be integer
index_Q = T.lvector()
index_A = T.lvector()
y = T.lvector()
len_D = T.lscalar()
len_D_s = T.lvector()
len_Q = T.lscalar()
len_A = T.lscalar()
left_D = T.lscalar()
left_D_s = T.lvector()
left_Q = T.lscalar()
left_A = T.lscalar()
right_D = T.lscalar()
right_D_s = T.lvector()
right_Q = T.lscalar()
right_A = T.lscalar()
#wmf=T.dmatrix()
cost_tmp = T.dscalar()
#x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten()
ishape = (emb_size, maxSentLength) # sentence shape
dshape = (nkerns[0], maxDocLength) # doc shape
filter_words = (emb_size, window_width)
filter_sents = (nkerns[0], window_width)
#poolsize1=(1, ishape[1]-filter_size[1]+1) #?????????????????????????????
# length_after_wideConv=ishape[1]+filter_size[1]-1
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape(
(maxDocLength, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_Q_input = embeddings[index_Q.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A_input = embeddings[index_A.flatten()].reshape(
(batch_size, maxSentLength,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b = create_conv_para(rng,
filter_shape=(nkerns[0], 1,
filter_words[0],
filter_words[1]))
# load_model_for_conv1([conv_W, conv_b])
layer0_D = Conv_with_input_para(
rng,
input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_Q = Conv_with_input_para(
rng,
input=layer0_Q_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_A = Conv_with_input_para(
rng,
input=layer0_A_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]),
W=conv_W,
b=conv_b)
layer0_D_output = debug_print(layer0_D.output, 'layer0_D.output')
layer0_Q_output = debug_print(layer0_Q.output, 'layer0_Q.output')
layer0_A_output = debug_print(layer0_A.output, 'layer0_A.output')
layer0_para = [conv_W, conv_b]
layer1_DQ = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_Q_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_Q,
right_r=right_Q,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_Q + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
layer1_DA = Average_Pooling_Scan(rng,
input_D=layer0_D_output,
input_r=layer0_A_output,
kern=nkerns[0],
left_D=left_D,
right_D=right_D,
left_D_s=left_D_s,
right_D_s=right_D_s,
left_r=left_A,
right_r=right_A,
length_D_s=len_D_s + filter_words[1] - 1,
length_r=len_A + filter_words[1] - 1,
dim=maxSentLength + filter_words[1] - 1,
doc_len=maxDocLength,
topk=3)
conv2_W, conv2_b = create_conv_para(rng,
filter_shape=(nkerns[1], 1, nkerns[0],
filter_sents[1]))
#load_model_for_conv2([conv2_W, conv2_b])#this can not be used, as the nkerns[0]!=filter_size[0]
#conv from sentence to doc
layer2_DQ = Conv_with_input_para(
rng,
input=layer1_DQ.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_DA = Conv_with_input_para(
rng,
input=layer1_DA.output_D.reshape(
(batch_size, 1, nkerns[0], dshape[1])),
image_shape=(batch_size, 1, nkerns[0], dshape[1]),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
#conv single Q and A into doc level with same conv weights
layer2_Q = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DQ.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_A = Conv_with_input_para_one_col_featuremap(
rng,
input=layer1_DA.output_QA_sent_level_rep.reshape(
(batch_size, 1, nkerns[0], 1)),
image_shape=(batch_size, 1, nkerns[0], 1),
filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]),
W=conv2_W,
b=conv2_b)
layer2_Q_output_sent_rep_Dlevel = debug_print(
layer2_Q.output_sent_rep_Dlevel, 'layer2_Q.output_sent_rep_Dlevel')
layer2_A_output_sent_rep_Dlevel = debug_print(
layer2_A.output_sent_rep_Dlevel, 'layer2_A.output_sent_rep_Dlevel')
layer2_para = [conv2_W, conv2_b]
layer3_DQ = Average_Pooling_for_Top(
rng,
input_l=layer2_DQ.output,
input_r=layer2_Q_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
layer3_DA = Average_Pooling_for_Top(
rng,
input_l=layer2_DA.output,
input_r=layer2_A_output_sent_rep_Dlevel,
kern=nkerns[1],
left_l=left_D,
right_l=right_D,
left_r=0,
right_r=0,
length_l=len_D + filter_sents[1] - 1,
length_r=1,
dim=maxDocLength + filter_sents[1] - 1,
topk=3)
#high-way
high_W, high_b = create_highw_para(rng, nkerns[0], nkerns[1])
transform_gate_DQ = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DQ.output_D_sent_level_rep) + high_b),
'transform_gate_DQ')
transform_gate_DA = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA.output_D_sent_level_rep) + high_b),
'transform_gate_DA')
transform_gate_Q = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DQ.output_QA_sent_level_rep) + high_b),
'transform_gate_Q')
transform_gate_A = debug_print(
T.nnet.sigmoid(
T.dot(high_W, layer1_DA.output_QA_sent_level_rep) + high_b),
'transform_gate_A')
highW_para = [high_W, high_b]
overall_D_Q = debug_print(
(1.0 - transform_gate_DQ) * layer1_DQ.output_D_sent_level_rep +
transform_gate_DQ * layer3_DQ.output_D_doc_level_rep, 'overall_D_Q')
overall_D_A = (
1.0 - transform_gate_DA
) * layer1_DA.output_D_sent_level_rep + transform_gate_DA * layer3_DA.output_D_doc_level_rep
overall_Q = (
1.0 - transform_gate_Q
) * layer1_DQ.output_QA_sent_level_rep + transform_gate_Q * layer2_Q.output_sent_rep_Dlevel
overall_A = (
1.0 - transform_gate_A
) * layer1_DA.output_QA_sent_level_rep + transform_gate_A * layer2_A.output_sent_rep_Dlevel
simi_sent_level = debug_print(
cosine(
layer1_DQ.output_D_sent_level_rep +
layer1_DA.output_D_sent_level_rep,
layer1_DQ.output_QA_sent_level_rep +
layer1_DA.output_QA_sent_level_rep), 'simi_sent_level')
simi_doc_level = debug_print(
cosine(
layer3_DQ.output_D_doc_level_rep +
layer3_DA.output_D_doc_level_rep,
layer2_Q.output_sent_rep_Dlevel + layer2_A.output_sent_rep_Dlevel),
'simi_doc_level')
simi_overall_level = debug_print(
cosine(overall_D_Q + overall_D_A, overall_Q + overall_A),
'simi_overall_level')
# eucli_1=1.0/(1.0+EUCLID(layer3_DQ.output_D+layer3_DA.output_D, layer3_DQ.output_QA+layer3_DA.output_QA))
layer4_input = debug_print(
T.concatenate([simi_sent_level, simi_doc_level, simi_overall_level],
axis=1),
'layer4_input') #, layer2.output, layer1.output_cosine], axis=1)
#layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1)
#layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2)
layer4 = LogisticRegression(rng, input=layer4_input, n_in=3, n_out=2)
#L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum()
L2_reg = debug_print(
(layer4.W**2).sum() + (high_W**2).sum() + (conv2_W**2).sum() +
(conv_W**2).sum(),
'L2_reg') #+(layer1.W** 2).sum()++(embeddings**2).sum()
cost_this = debug_print(layer4.negative_log_likelihood(y),
'cost_this') #+L2_weight*L2_reg
cost = debug_print(
(cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost')
#cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost')
#
# [train_data_D, train_data_Q, train_data_A, train_Y, train_Label,
# train_Length_D,train_Length_D_s, train_Length_Q, train_Length_A,
# train_leftPad_D,train_leftPad_D_s, train_leftPad_Q, train_leftPad_A,
# train_rightPad_D,train_rightPad_D_s, train_rightPad_Q, train_rightPad_A]=train_data
# [test_data_D, test_data_Q, test_data_A, test_Y, test_Label,
# test_Length_D,test_Length_D_s, test_Length_Q, test_Length_A,
# test_leftPad_D,test_leftPad_D_s, test_leftPad_Q, test_leftPad_A,
# test_rightPad_D,test_rightPad_D_s, test_rightPad_Q, test_rightPad_A]=test_data
# index = T.lscalar()
# index_D = T.lmatrix() # now, x is the index matrix, must be integer
# index_Q = T.lvector()
# index_A= T.lvector()
#
# y = T.lvector()
# len_D=T.lscalar()
# len_D_s=T.lvector()
# len_Q=T.lscalar()
# len_A=T.lscalar()
#
# left_D=T.lscalar()
# left_D_s=T.lvector()
# left_Q=T.lscalar()
# left_A=T.lscalar()
#
# right_D=T.lscalar()
# right_D_s=T.lvector()
# right_Q=T.lscalar()
# right_A=T.lscalar()
#
#
# #wmf=T.dmatrix()
# cost_tmp=T.dscalar()
test_model = theano.function(
[index],
[layer4.errors(y), layer4_input, y, layer4.prop_for_posi],
givens={
index_D: test_data_D[index], #a matrix
index_Q: test_data_Q[index],
index_A: test_data_A[index],
y: test_Y[index:index + batch_size],
len_D: test_Length_D[index],
len_D_s: test_Length_D_s[index],
len_Q: test_Length_Q[index],
len_A: test_Length_A[index],
left_D: test_leftPad_D[index],
left_D_s: test_leftPad_D_s[index],
left_Q: test_leftPad_Q[index],
left_A: test_leftPad_A[index],
right_D: test_rightPad_D[index],
right_D_s: test_rightPad_D_s[index],
right_Q: test_rightPad_Q[index],
right_A: test_rightPad_A[index]
},
on_unused_input='ignore')
#params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b]
params = layer4.params + layer2_para + layer0_para + highW_para
accumulator = []
for para_i in params:
eps_p = numpy.zeros_like(para_i.get_value(borrow=True),
dtype=theano.config.floatX)
accumulator.append(theano.shared(eps_p, borrow=True))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i, acc_i in zip(params, grads, accumulator):
grad_i = debug_print(grad_i, 'grad_i')
acc = acc_i + T.sqr(grad_i)
updates.append(
(param_i,
param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad
updates.append((acc_i, acc))
train_model = theano.function(
[index, cost_tmp],
cost,
updates=updates,
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index + batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
},
on_unused_input='ignore')
train_model_predict = theano.function(
[index], [cost_this, layer4.errors(y), layer4_input, y],
givens={
index_D: train_data_D[index],
index_Q: train_data_Q[index],
index_A: train_data_A[index],
y: train_Y[index:index + batch_size],
len_D: train_Length_D[index],
len_D_s: train_Length_D_s[index],
len_Q: train_Length_Q[index],
len_A: train_Length_A[index],
left_D: train_leftPad_D[index],
left_D_s: train_leftPad_D_s[index],
left_Q: train_leftPad_Q[index],
left_A: train_leftPad_A[index],
right_D: train_rightPad_D[index],
right_D_s: train_rightPad_D_s[index],
right_Q: train_rightPad_Q[index],
right_A: train_rightPad_A[index]
},
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 500000000000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
mid_time = start_time
epoch = 0
done_looping = False
max_acc = 0.0
best_epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#for minibatch_index in xrange(n_train_batches): # each batch
minibatch_index = 0
#shuffle(train_batch_start)#shuffle training data
cost_tmp = 0.0
# readfile=open('/mounts/data/proj/wenpeng/Dataset/SICK/train_plus_dev.txt', 'r')
# train_pairs=[]
# train_y=[]
# for line in readfile:
# tokens=line.strip().split('\t')
# listt=tokens[0]+'\t'+tokens[1]
# train_pairs.append(listt)
# train_y.append(tokens[2])
# readfile.close()
# writefile=open('/mounts/data/proj/wenpeng/Dataset/SICK/weights_fine_tune.txt', 'w')
for batch_start in train_batch_start:
# iter means how many batches have been runed, taking into loop
iter = (epoch - 1) * n_train_batches + minibatch_index + 1
sys.stdout.write("Training :[%6f] %% complete!\r" %
(batch_start * 100.0 / train_size))
sys.stdout.flush()
minibatch_index = minibatch_index + 1
#if epoch %2 ==0:
# batch_start=batch_start+remain_train
#time.sleep(0.5)
#print batch_start
if iter % update_freq != 0:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
#print 'layer3_input', layer3_input
cost_tmp += cost_ij
error_sum += error_ij
else:
cost_average = train_model(batch_start, cost_tmp)
#print 'layer3_input', layer3_input
error_sum = 0
cost_tmp = 0.0 #reset for the next batch
#print 'cost_average ', cost_average
#print 'cost_this ',cost_this
#exit(0)
#exit(0)
if iter % n_train_batches == 0:
print 'training @ iter = ' + str(
iter) + ' average cost: ' + str(cost_average)
if iter % validation_frequency == 0:
#write_file=open('log.txt', 'w')
test_losses = []
test_y = []
test_features = []
test_prop = []
for i in test_batch_start:
test_loss, layer3_input, y, posi_prop = test_model(i)
test_prop.append(posi_prop[0][0])
#test_losses = [test_model(i) for i in test_batch_start]
test_losses.append(test_loss)
test_y.append(y[0])
test_features.append(layer3_input[0])
#write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+
#write_file.close()
#test_score = numpy.mean(test_losses)
test_acc = compute_test_acc(test_y, test_prop)
#test_acc=1-test_score
print(
('\t\t\tepoch %i, minibatch %i/%i, test acc of best '
'model %f %%') %
(epoch, minibatch_index, n_train_batches, test_acc * 100.))
#now, see the results of LR
#write_feature=open(rootPath+'feature_check.txt', 'w')
train_y = []
train_features = []
count = 0
for batch_start in train_batch_start:
cost_ij, error_ij, layer3_input, y = train_model_predict(
batch_start)
train_y.append(y[0])
train_features.append(layer3_input[0])
#write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n')
#count+=1
#write_feature.close()
clf = svm.SVC(
kernel='linear'
) #OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33
clf.fit(train_features, train_y)
results = clf.decision_function(test_features)
lr = linear_model.LogisticRegression(C=1e5)
lr.fit(train_features, train_y)
results_lr = lr.decision_function(test_features)
acc_svm = compute_test_acc(test_y, results)
acc_lr = compute_test_acc(test_y, results_lr)
find_better = False
if acc_svm > max_acc:
max_acc = acc_svm
best_epoch = epoch
find_better = True
if test_acc > max_acc:
max_acc = test_acc
best_epoch = epoch
find_better = True
if acc_lr > max_acc:
max_acc = acc_lr
best_epoch = epoch
find_better = True
print '\t\t\tsvm:', acc_svm, 'lr:', acc_lr, 'nn:', test_acc, 'max:', max_acc, '(at', best_epoch, ')'
# if find_better==True:
# store_model_to_file(layer2_para, best_epoch)
# print 'Finished storing best conv params'
if patience <= iter:
done_looping = True
break
print 'Epoch ', epoch, 'uses ', (time.clock() - mid_time) / 60.0, 'min'
mid_time = time.clock()
#writefile.close()
#print 'Batch_size: ', update_freq
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.09, 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.))
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
#layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1]))
layer0_D_input = embeddings[index_D.flatten()].reshape((maxDocLength,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A1_input = embeddings[index_A1.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A2_input = embeddings[index_A2.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
layer0_A3_input = embeddings[index_A3.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]))
layer0_para=[conv_W, conv_b]
conv2_W, conv2_b=create_conv_para(rng, filter_shape=(nkerns[1], 1, nkerns[0], filter_sents[1]))
layer2_para=[conv2_W, conv2_b]
high_W, high_b=create_highw_para(rng, nkerns[0], nkerns[1]) # this part decides nkern[0] and nkern[1] must be in the same dimension
highW_para=[high_W, high_b]
params = layer2_para+layer0_para+highW_para#+[embeddings]
layer0_D = Conv_with_input_para(rng, input=layer0_D_input,
image_shape=(maxDocLength, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A1 = Conv_with_input_para(rng, input=layer0_A1_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
filter_shape=(nkerns[0], 1, filter_words[0], filter_words[1]), W=conv_W, b=conv_b)
layer0_A2 = Conv_with_input_para(rng, input=layer0_A2_input,
image_shape=(batch_size, 1, ishape[0], ishape[1]),
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)
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
'''
