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cnn_sentiment.py
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cnn_sentiment.py
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#!/usr/bin/env python3
# coding: utf-8
# File: cnn_sentiment.py
# Author: lhy<[email protected],https://huangyong.github.io>
# Date: 18-3-19
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):
word_list = [word for word in sentence.split(' ')]
max_words = 100
embedding_dim = 200
embedding_matrix = np.zeros((max_words, embedding_dim))
for index, word in enumerate(word_list):
try:
embedding_matrix[index] = model[word]
except:
pass
return embedding_matrix
'''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),
'''四层CNN进行训练，迭代20次'''
def train_cnn(X_train, Y_train, X_test, Y_test):
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
#建立sequential序贯模型
model = Sequential()
#input_shape = (rows行, cols列, 1) 1表示颜色通道数目, rows行，对应一句话的长度, cols列表示词向量的维度
model.add(Conv1D(64, 3, activation='relu', input_shape=(100, 200)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(GlobalAveragePooling1D())
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_cnn_model.h5')
'''
1 [==============================] - 13s 664us/step - loss: 0.4868 - acc: 0.7645 - val_loss: 0.3897 - val_acc: 0.8234
5 [==============================] - 13s 633us/step - loss: 0.2923 - acc: 0.8794 - val_loss: 0.3376 - val_acc: 0.8527
10 [==============================] - 12s 601us/step - loss: 0.1337 - acc: 0.9482 - val_loss: 0.5124 - val_acc: 0.8284
15 [==============================] - 13s 631us/step - loss: 0.0729 - acc: 0.9789 - val_loss: 0.8681 - val_acc: 0.8325
20 [==============================] - 13s 632us/step - loss: 0.0484 - acc: 0.9873 - val_loss: 1.0889 - val_acc: 0.8376
'''
'''实际应用，测试'''
def predict_cnn(model_filepath):
model = load_model(model_filepath)
sentence = '这个 电视 真 尼玛 垃圾 ， 老子 再也 不买 了' # [[2.3127215e-04 0.9977249]]
sentence = '这件 衣服 真的 太 好看 了 ！ 好想 买 啊 ' # [[0.9936581 0.00627225]]
sentence_vector = np.array([rep_sentencevector(sentence)])
print(sentence_vector)
print('test after load: ', model.predict(sentence_vector))
if __name__ == '__main__':
# X_train, Y_train, X_test, Y_test = build_traindata()
model_filepath = './model/sentiment_cnn_model.h5'
# print(X_train.shape, Y_train.shape)
# print(X_test.shape, Y_test.shape)
# train_cnn(X_train, Y_train, X_test, Y_test)
predict_cnn(model_filepath)