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mlp_sentiment.py
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mlp_sentiment.py
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#!/usr/bin/env python3
# coding: utf-8
# File: mlp_sentiment.py
# Author: lhy<[email protected],https://huangyong.github.io>
# Date: 18-3-20
#!/usr/bin/env python3
import gensim
import numpy as np
from keras.models import load_model
VECTOR_DIR = './embedding/word_vector.bin' # 词向量模型文件
model = gensim.models.KeyedVectors.load_word2vec_format(VECTOR_DIR, binary=False)
'''基于wordvector，通过lookup table的方式找到句子的wordvector的表示'''
def rep_sentencevector(sentence):
'''通过向量求和的方式标识sentence vector'''
word_list = [word for word in sentence.split(' ')]
embedding_dim = 200
embedding_matrix = np.zeros(embedding_dim)
for index, word in enumerate(word_list):
try:
embedding_matrix += model[word]
except:
pass
return embedding_matrix/len(word_list)
'''构造训练数据'''
def build_traindata():
X_train = list()
Y_train = list()
X_test = list()
Y_test = list()
for line in open('./data/train.txt'):
line = line.strip().strip().split('\t')
sent_vector = rep_sentencevector(line[-1])
X_train.append(sent_vector)
if line[0] == '1':
Y_train.append([0, 1])
else:
Y_train.append([1, 0])
for line in open('./data/test.txt'):
line = line.strip().strip().split('\t')
sent_vector = rep_sentencevector(line[-1])
X_test.append(sent_vector)
if line[0] == '1':
Y_test.append([0, 1])
else:
Y_test.append([1, 0])
return np.array(X_train), np.array(Y_train), np.array(X_test), np.array(Y_test),
'''三层mlp进行训练，迭代20次'''
def train_mlp(X_train, Y_train, X_test, Y_test):
from keras.models import Sequential
from keras.layers import Dense, Dropout
model = Sequential()
model.add(Dense(64, input_dim=(200), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=100, epochs=20, validation_data=(X_test, Y_test))
model.save('./model/sentiment_mlp_model.h5')
'''
1 [==============================] - 1s 25us/step - loss: 1.7893 - acc: 0.6168 - val_loss: 0.5576 - val_acc: 0.7076
5 [==============================] - 0s 19us/step - loss: 0.4499 - acc: 0.7987 - val_loss: 0.4056 - val_acc: 0.8204
10 [==============================] - 0s 17us/step - loss: 0.4043 - acc: 0.8274 - val_loss: 0.4016 - val_acc: 0.8341
15 [==============================] - 0s 17us/step - loss: 0.3815 - acc: 0.8397 - val_loss: 0.3821 - val_acc: 0.8345
20 [==============================] - 0s 17us/step - loss: 0.3746 - acc: 0.8432 - val_loss: 0.3842 - val_acc: 0.8359
'''
'''实际应用，测试'''
def predict_mlp(model_filepath):
model = load_model(model_filepath)
sentence1 = '这个 电视 真 尼玛 垃圾 ， 老子 再也 不买 了' # [[0.0942708 0.9058427]]
sentence2 = '这件 衣服 真的 太 好看 了 ！ 好想 买 啊 ' # [[0.6489922 0.34993422]]
sentence_vector1 = np.array([rep_sentencevector(sentence1)])
sentence_vector2 = np.array([rep_sentencevector(sentence2)])
print('test after load: ', model.predict(sentence_vector1))
print('test after load: ', model.predict(sentence_vector2))
if __name__ == '__main__':
#X_train, Y_train, X_test, Y_test = build_traindata()
model_filepath = './model/sentiment_mlp_model.h5'
#print(X_train.shape, Y_train.shape)
#print(X_test.shape, Y_test.shape)
#train_mlp(X_train, Y_train, X_test, Y_test)
predict_mlp(model_filepath)