tensorflow 实践（一）使用神经网络做中文情感分析详解大数据

本文使用哈工大做文本预处理； 两层隐层神经网络；
后注：不是标准的ann，做了去停用词和词性筛选，没有端到端。

# -*- coding: utf-8 -*- 
# @bref :使用tensorflow做中文情感分析 
import numpy as np 
import tensorflow as tf 
import random 
from sklearn.feature_extraction.text import CountVectorizer 
import os 
import traceback 
real_dir_path = os.path.split(os.path.realpath(__file__))[0] 
pos_file = os.path.join(real_dir_path, 'data/pos_bak.txt') 
neg_file = os.path.join(real_dir_path, 'data/neg_bak.txt') 
#使用哈工大分词和词性标注 
from pyltp import Segmentor, Postagger 
seg = Segmentor() 
seg.load('/root/git/ltp_data/cws.model') 
poser = Postagger() 
poser.load('/root/git/ltp_data/pos.model') 
real_dir_path = os.path.split(os.path.realpath(__file__))[0] #文件所在路径 
stop_words_file = os.path.join(real_dir_path, '../util/stopwords.txt') 
#定义允许的词性 
allow_pos_ltp = ('a', 'i', 'j', 'n', 'nh', 'ni', 'nl', 'ns', 'nt', 'nz', 'v', 'ws') 
#分词、去除停用词、词性筛选 
def cut_stopword_pos(s): 
words = seg.segment(''.join(s.split())) 
poses = poser.postag(words) 
stopwords = {}.fromkeys([line.rstrip() for line in open(stop_words_file)]) 
sentence = [] 
for i, pos in enumerate(poses): 
if (pos in allow_pos_ltp) and (words[i] not in stopwords): 
sentence.append(words[i]) 
return sentence 
def create_vocab(pos_file, neg_file): 
def process_file(file_path): 
with open(file_path, 'r') as f: 
v = [] 
lines = f.readlines() 
for line in lines: 
sentence = cut_stopword_pos(line) 
v.append(' '.join(sentence)) 
return v 
sent = process_file(pos_file) 
sent += process_file(neg_file) 
tf_v = CountVectorizer(max_df=0.9, min_df=1) 
tf = tf_v.fit_transform(sent) 
#print tf_v.vocabulary_ 
return tf_v.vocabulary_.keys() 
#获取词汇 
vocab = create_vocab(pos_file, neg_file) 
#依据词汇将评论转化为向量 
def normalize_dataset(vocab): 
dataset = [] 
# vocab:词汇表; review:评论; clf:评论对应的分类, [0, 1]表示负面评论,[1, 0]表示正面 
def string_to_vector(vocab, review, clf): 
words = cut_stopword_pos(review) # list of str 
features = np.zeros(len(vocab)) 
for w in words: 
if w.decode('utf-8') in vocab: 
features[vocab.index(w.decode('utf-8'))] = 1 
return [features, clf] 
with open(pos_file, 'r') as f: 
lines = f.readlines() 
for line in lines: 
one_sample = string_to_vector(vocab, line, [1, 0]) 
dataset.append(one_sample) 
with open(neg_file, 'r') as f: 
lines = f.readlines() 
for line in lines: 
one_sample = string_to_vector(vocab, line, [0, 1]) 
dataset.append(one_sample) 
return dataset 
dataset = normalize_dataset(vocab) 
random.shuffle(dataset)  #打乱顺序 
#取样本的10%作为测试数据 
test_size = int(len(dataset) * 0.1) 
dataset = np.array(dataset) 
train_dataset = dataset[:-test_size] 
test_dataset = dataset[-test_size:] 
print 'test_size = {}'.format(test_size) 
#print 'size of train_dataset is {}'.format(train_dataset) 
#Feed-forward nueral network 
#定义每个层有多少个神经元 
n_input_layer = len(vocab)   #输入层每个神经元代表一个term 
n_layer_1 = 1000  #hiden layer 
n_layer_2 = 1000 # hiden layer 
n_output_layer = 2 
#定义待训练的神经网络 
def neural_netword(data): 
#定义第一层神经元的w和b, random_normal定义服从正态分布的随机变量 
layer_1_w_b = {'w_':tf.Variable(tf.random_normal([n_input_layer, n_layer_1])), 'b_':tf.Variable(tf.random_normal([n_layer_1]))} 
layer_2_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_1, n_layer_2])), 'b_':tf.Variable(tf.random_normal([n_layer_2]))} 
layer_output_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_2, n_output_layer])), 'b_':tf.Variable(tf.random_normal([n_output_layer]))} 
layer_1 = tf.add(tf.matmul(data, layer_1_w_b['w_']), layer_1_w_b['b_']) 
layer_1 = tf.nn.relu(layer_1) #relu做激活函数 
layer_2 = tf.add(tf.matmul(layer_1, layer_2_w_b['w_']), layer_2_w_b['b_']) 
layer_2 = tf.nn.relu(layer_2) 
layer_output = tf.add(tf.matmul(layer_2, layer_output_w_b['w_']), layer_output_w_b['b_']) 
return layer_output 
batch_size = 50 
X = tf.placeholder('float', [None, n_input_layer])  #None表示样本数量任意; 每个样本纬度是term数量 
Y = tf.placeholder('float') 
#使用数据训练神经网络 
def train_neural_network(X, Y): 
predict = neural_netword(X) 
#cost func是输出层softmax的cross entropy的平均值。 将softmax 放在此处而非nn中是为了效率. 
cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=Y)) 
#设置优化器 
optimizer = tf.train.AdamOptimizer().minimize(cost_func) 
epochs = 13  #epoch本意是时代、纪, 这里是迭代周期 
with tf.Session() as session: 
session.run(tf.initialize_all_variables()) #初始化所有变量,包括w,b 
random.shuffle(train_dataset) 
train_x = train_dataset[:, 0] #每一行的features; 
train_y = train_dataset[:, 1] #每一行的label 
print 'size of train_x is {}'.format(len(train_x)) 
for epoch in range(epochs): 
epoch_loss = 0 #每个周期的loss 
i = 0 
while i < len(train_x): 
start = i 
end = i + batch_size 
batch_x = train_x[start:end] 
batch_y = train_y[start:end] 
#run的第一个参数fetches可以是单个,也可以是多个。 返回值是fetches的返回值。 
#此处因为要打印cost,所以cost_func也在fetches中 
_, c = session.run([optimizer, cost_func], feed_dict={X:list(batch_x), Y:list(batch_y)}) 
epoch_loss += c 
i = end 
print(epoch, ' : ', epoch_loss) 
#评估模型 
test_x = test_dataset[:, 0] 
test_y = test_dataset[:, 1] 
#argmax能给出某个tensor对象在某一维上的其数据最大值所在的索引值, 这里是索引值的list。tf.equal用于检测匹配,返回bool型的list 
correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1)) 
#tf.cast 可以将[True, False, True] 转化为[1, 0, 1] 
#reduce_mean用于在某一维上计算平均值, 未指定纬度则计算所有元素 
accurqcy = tf.reduce_mean(tf.cast(correct, 'float')) 
print('准确率: {}'.format(accurqcy.eval({X:list(test_x), Y:list(test_y)}))) 
#等价: print session.run(accuracy, feed_dict={X:list(test_x), Y:list(test_y)}) 
train_neural_network(X, Y) 

最终的执行显示：

size of train_x is 31612 
(0, ' : ', 105508.38228607178) 
(1, ' : ', 11773.463727131188) 
(2, ' : ', 4551.4978754326503) 
(3, ' : ', 3576.6907950473492) 
(4, ' : ', 3144.6771814899175) 
(5, ' : ', 2911.1803286887775) 
(6, ' : ', 2691.8284285693276) 
(7, ' : ', 2651.9982114042473) 
(8, ' : ', 2882.4479921576026) 
(9, ' : ', 2665.3818837262743) 
(10, ' : ', 2551.3030235993206) 
(11, ' : ', 2838.3546982686303) 
(12, ' : ', 2770.5539811982608) 
准确率: 0.828587830067