tensorflow 实践（一）使用神经网络做中文情感分析

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 npimport tensorflow as tfimport randomfrom sklearn.feature_extraction.text import CountVectorizerimport osimport tracebackreal_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, Postaggerseg = 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 sentencedef 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 datasetdataset = 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termn_layer_1 = 1000  #hiden layern_layer_2 = 1000 # hiden layern_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_outputbatch_size = 50X = 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