-
Notifications
You must be signed in to change notification settings - Fork 2
/
siamese_lstm.py
202 lines (157 loc) · 6.37 KB
/
siamese_lstm.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
# ------------------------------------------------- #
# Siamese LSTM for word-paraphrase similarity #
# Jun 24, 2015, 1655 IST #
# Vijay Prakash Dwivedi #
#-------------------------------------------------- #
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import os
import gc
from keras.preprocessing import sequence
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional, Input, Lambda
from keras.datasets import imdb
from keras.layers.embeddings import Embedding
from keras.optimizers import RMSprop, Adamax
from keras import backend as K
import tensorflow as tf
# custom module to read activations of layers
from read_activations import get_activations
# ignore TensorFlow messages and warnings
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
# os.system('clear')
# fix random seed for reproducibility
np.random.seed(7)
# importing custom module for data preprocessing
import preprocess_data_lstm
vocab_size = preprocess_data_lstm.vocab_size()
emb_dim = preprocess_data_lstm.embed_dim()
def euclidean_distance(vects):
x, y = vects
return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
# return K.abs(x-y)
def eucl_dist_output_shape(shapes):
shape1, shape2 = shapes
return (shape1[0], 1)
def contrastive_loss(y_true, y_pred):
'''Contrastive loss from Hadsell-et-al.'06
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
'''
margin = 1
return K.mean(y_true * K.square(y_pred) +
(1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
def compute_accuracy(predictions, labels):
'''Compute classification accuracy with a fixed threshold on distances.
'''
# return labels[predictions.ravel() < 0.5].mean()
return np.mean(np.equal(predictions.ravel() < 0.5, labels))
def create_base_network(timesteps, input_dim):
'''Base LSTM network to be shared.
'''
seq = Sequential()
# seq.add(LSTM(32, input_shape=(int(input_dim/emb_dim), emb_dim)))
seq.add(LSTM(768, return_sequences=True, input_shape=(timesteps, input_dim)))
seq.add(Dropout(0.6))
seq.add(LSTM(512, return_sequences=True))
seq.add(Dropout(0.6))
seq.add(LSTM(300))
# seq.add(Dropout(0.6))
return seq
# sts_pairs = preprocess_data_lstm.getSemEval()
# print(sts_pairs.shape)
x_data, y_data, visualise_pairs = preprocess_data_lstm.dataset()
input_dim = 1000
epochs = 50
timesteps = int(input_dim/emb_dim)
input_dim = emb_dim
train_test_split = 400000 # 263000
tr_pairs = x_data[:train_test_split] # 263000
tr_y = y_data[:train_test_split]
te_pairs = x_data[train_test_split:] # 113000
te_y = y_data[train_test_split:]
print(tr_pairs[1, 1].shape)
base_network = create_base_network(timesteps, input_dim)
print(base_network.summary())
input_a = Input(shape=(timesteps, input_dim))
input_b = Input(shape=(timesteps, input_dim))
# because we re-use the same instance `base_network`,
# the weights of the network
# will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)
distance = Lambda(euclidean_distance,
output_shape=eucl_dist_output_shape)([processed_a, processed_b])
# model = Model([input_a, input_b], distance)
model = load_model('checkpoints/model_siamese_lstm_0.766548672566.h5')
# # train
# rms = RMSprop()
# opt = Adamax()
# model.compile(loss=contrastive_loss, optimizer=opt)
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
batch_size=384,
epochs=epochs)
# validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))
print(model.summary())
# compute final accuracy on training and test sets
pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
tr_acc = compute_accuracy(pred, tr_y)
pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
te_acc = compute_accuracy(pred, te_y)
print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
model.save('checkpoints/model_siamese_lstm_'+str(te_acc)+'.h5')
# sts_result = []
# pred = model.predict([sts_pairs[:, 0], sts_pairs[:, 1]])
# for item in pred:
# for res in item:
# sts_result.append(res*5.0)
# for res in sts_result:
# print(res)
data_for_test = visualise_pairs
# Storing embeddings in a file
file_word = open('embeddings_word', 'a')
file_paraphrase = open('embeddings_paraphrase', 'a')
words = preprocess_data_lstm.getWords()
paraphrase = preprocess_data_lstm.get_pp()
ptr = 0
batch_sz = 1000
nm_batch = 300
for j in range(nm_batch):
L, R = data_for_test[ptr:ptr+batch_sz, 0], data_for_test[ptr:ptr+batch_sz, 1]
functor = K.function([input_a, input_b]+ [K.learning_phase()], [processed_a, processed_b])
layer_out = functor([L, R, 0.])
word_embd = layer_out[0]
paraphrase_embd = layer_out[1]
k = 0
for i in range(ptr, ptr+batch_sz):
file_word.write(words[i]+" -- "+' '.join(map(str, word_embd[k]))+'\n')
file_paraphrase.write(paraphrase[i]+" -- "+' '.join(map(str, paraphrase_embd[k]))+'\n')
k += 1
ptr += batch_sz
file_word.close()
file_paraphrase.close()
############################################################################
# T E S T P A R T #
############################################################################
# paraphrase_embedding = tf.stack(layer_out[1])
# batch_word_embedding = tf.stack(layer_out[0][14450:14500])
# norm_paraphrase_embedding = tf.nn.l2_normalize(paraphrase_embedding, dim=1)
# norm_batch_word_embedding = tf.nn.l2_normalize(batch_word_embedding, dim=1)
# cosine_similarity = tf.matmul(norm_batch_word_embedding, tf.transpose(norm_paraphrase_embedding, [1, 0]))
# # closest_words = tf.argmax(cosine_similarity, 1)
# tf.InteractiveSession()
# sim = cosine_similarity.eval()
# words = preprocess_data_lstm.getWords()
# paraphrase = preprocess_data_lstm.get_pp()
# for i in range(50):
# valid_word = words[i+14450]
# # print(valid_word)
# top_k = 8 # number of nearest neighbours
# nearest = (-sim[i, :]).argsort()[1:top_k + 1]
# log_str = "Nearest to '%s':" % valid_word
# for k in range(top_k):
# close_word = paraphrase[nearest[k]]
# log_str = "%s %s," % (log_str, close_word)
# print(log_str+"\n")
gc.collect()