forked from songyouwei/SA-DL
/
lstm.py
97 lines (87 loc) · 4.27 KB
/
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
# -*- coding: utf-8 -*-
# file: lstm.py
# author: songyouwei <youwei0314@gmail.com>
# Copyright (C) 2018. All Rights Reserved.
from __future__ import print_function
import argparse
import os
from tensorflow.python.keras.callbacks import TensorBoard, LambdaCallback
# from tensorflow.python.keras.utils import plot_model
from utils import read_dataset
from custom_metrics import f1
from tensorflow.python.keras import initializers, regularizers, optimizers
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from tensorflow.python.keras.models import Model, load_model
from tensorflow.python.keras.layers import Input, Dense, Activation, LSTM, Embedding, Bidirectional
class RawLSTM:
def __init__(self):
self.DATASET = 'twitter' # 'twitter', 'restaurant', 'laptop'
self.POLARITIES_DIM = 3
self.EMBEDDING_DIM = 100
self.LEARNING_RATE = 0.01
self.INITIALIZER = initializers.RandomUniform(minval=-0.003, maxval=0.003)
self.REGULARIZER = regularizers.l2(0.001)
self.LSTM_PARAMS = {
'units': 200,
'activation': 'tanh',
'recurrent_activation': 'sigmoid',
'kernel_initializer': self.INITIALIZER,
'recurrent_initializer': self.INITIALIZER,
'bias_initializer': self.INITIALIZER,
'kernel_regularizer': self.REGULARIZER,
'recurrent_regularizer': self.REGULARIZER,
'bias_regularizer': self.REGULARIZER,
'dropout': 0,
'recurrent_dropout': 0,
}
self.MAX_SEQUENCE_LENGTH = 80
self.MAX_ASPECT_LENGTH = 10
self.BATCH_SIZE = 200
self.EPOCHS = 100
self.texts_raw_indices, self.texts_raw_without_aspects_indices, self.texts_left_indices, self.texts_left_with_aspects_indices, \
self.aspects_indices, self.texts_right_indices, self.texts_right_with_aspects_indices, \
self.polarities_matrix, \
self.embedding_matrix, \
self.tokenizer = \
read_dataset(type=self.DATASET,
mode='train',
embedding_dim=self.EMBEDDING_DIM,
max_seq_len=self.MAX_SEQUENCE_LENGTH, max_aspect_len=self.MAX_ASPECT_LENGTH)
if os.path.exists('lstm_saved_model.h5'):
print('loading saved model...')
self.model = load_model('lstm_saved_model.h5')
else:
print('Build model...')
inputs = Input(shape=(self.MAX_SEQUENCE_LENGTH,))
x = Embedding(input_dim=len(self.tokenizer.word_index) + 1,
output_dim=self.EMBEDDING_DIM,
input_length=self.MAX_SEQUENCE_LENGTH,
weights=[self.embedding_matrix],
trainable=False)(inputs)
x = LSTM(**self.LSTM_PARAMS)(x)
x = Dense(self.POLARITIES_DIM)(x)
predictions = Activation('softmax')(x)
model = Model(inputs, predictions)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=self.LEARNING_RATE), metrics=['acc', f1])
# plot_model(model, to_file='model.png')
self.model = model
def train(self):
tbCallBack = TensorBoard(log_dir='./lstm_logs', histogram_freq=0, write_graph=True, write_images=True)
def modelSave(epoch, logs):
if (epoch + 1) % 5 == 0:
self.model.save('lstm_saved_model.h5')
msCallBack = LambdaCallback(on_epoch_end=modelSave)
texts_raw_indices, texts_raw_without_aspects_indices, texts_left_indices, texts_left_with_aspects_indices, \
aspects_indices, texts_right_indices, texts_right_with_aspects_indices, \
polarities_matrix = \
read_dataset(type=self.DATASET,
mode='test',
embedding_dim=self.EMBEDDING_DIM,
max_seq_len=self.MAX_SEQUENCE_LENGTH, max_aspect_len=self.MAX_ASPECT_LENGTH)
self.model.fit(self.texts_raw_indices, self.polarities_matrix,
validation_data=(texts_raw_indices, polarities_matrix),
epochs=self.EPOCHS, batch_size=self.BATCH_SIZE, callbacks=[tbCallBack])
if __name__ == '__main__':
model = RawLSTM()
model.train()