-
Notifications
You must be signed in to change notification settings - Fork 195
/
lstm_train.py
167 lines (155 loc) · 6.71 KB
/
lstm_train.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
#!/usr/bin/env python3
# coding: utf-8
# File: lstm_train.py
# Author: lhy<lhy_in_blcu@126.com,https://huangyong.github.io>
# Date: 18-12-24
import numpy as np
from keras import backend as K
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, Bidirectional, LSTM, Dense, TimeDistributed, Dropout
from keras_contrib.layers.crf import CRF
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
class LSTMNER:
def __init__(self):
cur = '/'.join(os.path.abspath(__file__).split('/')[:-1])
self.train_path = os.path.join(cur, 'data/train.txt')
self.vocab_path = os.path.join(cur, 'model/vocab.txt')
self.embedding_file = os.path.join(cur, 'model/token_vec_300.bin')
self.model_path = os.path.join(cur, 'model/tokenvec_bilstm2_crf_model_20.h5')
self.datas, self.word_dict = self.build_data()
self.class_dict ={
'O':0,
'TREATMENT-I': 1,
'TREATMENT-B': 2,
'BODY-B': 3,
'BODY-I': 4,
'SIGNS-I': 5,
'SIGNS-B': 6,
'CHECK-B': 7,
'CHECK-I': 8,
'DISEASE-I': 9,
'DISEASE-B': 10
}
self.EMBEDDING_DIM = 300
self.EPOCHS = 5
self.BATCH_SIZE = 128
self.NUM_CLASSES = len(self.class_dict)
self.VOCAB_SIZE = len(self.word_dict)
self.TIME_STAMPS = 150
self.embedding_matrix = self.build_embedding_matrix()
'''构造数据集'''
def build_data(self):
datas = []
sample_x = []
sample_y = []
vocabs = {'UNK'}
for line in open(self.train_path):
line = line.rstrip().split('\t')
if not line:
continue
char = line[0]
if not char:
continue
cate = line[-1]
sample_x.append(char)
sample_y.append(cate)
vocabs.add(char)
if char in ['。','?','!','！','？']:
datas.append([sample_x, sample_y])
sample_x = []
sample_y = []
word_dict = {wd:index for index, wd in enumerate(list(vocabs))}
self.write_file(list(vocabs), self.vocab_path)
return datas, word_dict
'''将数据转换成keras所需的格式'''
def modify_data(self):
x_train = [[self.word_dict[char] for char in data[0]] for data in self.datas]
y_train = [[self.class_dict[label] for label in data[1]] for data in self.datas]
x_train = pad_sequences(x_train, self.TIME_STAMPS)
y = pad_sequences(y_train, self.TIME_STAMPS)
y_train = np.expand_dims(y, 2)
return x_train, y_train
'''保存字典文件'''
def write_file(self, wordlist, filepath):
with open(filepath, 'w+') as f:
f.write('\n'.join(wordlist))
'''加载预训练词向量'''
def load_pretrained_embedding(self):
embeddings_dict = {}
with open(self.embedding_file, 'r') as f:
for line in f:
values = line.strip().split(' ')
if len(values) < 300:
continue
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_dict[word] = coefs
print('Found %s word vectors.' % len(embeddings_dict))
return embeddings_dict
'''加载词向量矩阵'''
def build_embedding_matrix(self):
embedding_dict = self.load_pretrained_embedding()
embedding_matrix = np.zeros((self.VOCAB_SIZE + 1, self.EMBEDDING_DIM))
for word, i in self.word_dict.items():
embedding_vector = embedding_dict.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
return embedding_matrix
'''使用预训练向量进行模型训练'''
def tokenvec_bilstm2_crf_model(self):
model = Sequential()
embedding_layer = Embedding(self.VOCAB_SIZE + 1,
self.EMBEDDING_DIM,
weights=[self.embedding_matrix],
input_length=self.TIME_STAMPS,
trainable=False,
mask_zero=True)
model.add(embedding_layer)
model.add(Bidirectional(LSTM(128, return_sequences=True)))
model.add(Dropout(0.5))
model.add(Bidirectional(LSTM(64, return_sequences=True)))
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(self.NUM_CLASSES)))
crf_layer = CRF(self.NUM_CLASSES, sparse_target=True)
model.add(crf_layer)
model.compile('adam', loss=crf_layer.loss_function, metrics=[crf_layer.accuracy])
model.summary()
return model
'''训练模型'''
def train_model(self):
x_train, y_train = self.modify_data()
model = self.tokenvec_bilstm2_crf_model()
history = model.fit(x_train[:], y_train[:], validation_split=0.2, batch_size=self.BATCH_SIZE, epochs=self.EPOCHS)
self.draw_train(history)
model.save(self.model_path)
return model
'''绘制训练曲线'''
def draw_train(self, history):
# Plot training & validation accuracy values
plt.plot(history.history['acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train'], loc='upper left')
plt.show()
# 7836/7836 [==============================] - 205s 26ms/step - loss: 17.1782 - acc: 0.9624
'''
6268/6268 [==============================] - 145s 23ms/step - loss: 18.5272 - acc: 0.7196 - val_loss: 15.7497 - val_acc: 0.8109
6268/6268 [==============================] - 142s 23ms/step - loss: 17.8446 - acc: 0.9099 - val_loss: 15.5915 - val_acc: 0.8378
6268/6268 [==============================] - 136s 22ms/step - loss: 17.7280 - acc: 0.9485 - val_loss: 15.5570 - val_acc: 0.8364
6268/6268 [==============================] - 133s 21ms/step - loss: 17.6918 - acc: 0.9593 - val_loss: 15.5187 - val_acc: 0.8451
6268/6268 [==============================] - 144s 23ms/step - loss: 17.6723 - acc: 0.9649 - val_loss: 15.4944 - val_acc: 0.8451
'''
if __name__ == '__main__':
ner = LSTMNER()
ner.train_model()