tensorflow构建神经网络文本分类2

完成前面博客中配置好的环境：ubuntu14.04，tensorflow1.1 ，python3,matplotlib 2.02

利用神经网络对文本进行分类

#coding:utf-8from create_sentiment_featuresets import create_feature_sets_and_labelsimport numpy as npimport tensorflow as tfimport pickleimport matplotlib.pyplot as plt#从pickle文件读取数据pkl_file = open('sentiment_set.pickle', 'rb')train_x, train_y, test_x, test_y = pickle.load(pkl_file)n_nodes_hl1 = 128n_nodes_hl2 = 256n_nodes_hl3 = 512n_classes = 2batch_size = 100learning_rate=0.01epoch_plt = []accuracy_plt = []cost_plt = []x = tf.placeholder('float', [None, len(train_x[0])])y = tf.placeholder('float')#构建神经网络def neural_network_model(data):    hidden_1_layer = {                      'weights': tf.Variable(tf.random_normal( [len(train_x[0]), n_nodes_hl1],mean=0,stddev=1)),                      'biases': tf.Variable(tf.zeros([n_nodes_hl1]))                      }    hidden_2_layer = {                      'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2],mean=0,stddev=1)),                      'biases': tf.Variable(tf.zeros([n_nodes_hl2]))                      }    hidden_3_layer = {                      'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3],mean=0,stddev=1)),                      'biases': tf.Variable(tf.zeros([n_nodes_hl3]))                      }    output_layer = {                       'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes],mean=0,stddev=1)),                       'biases': tf.Variable(tf.zeros([n_classes]))                    }    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])    l1 = tf.nn.relu(l1)    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])    l2 = tf.nn.relu(l2)    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])    l3 = tf.nn.relu(l3)    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']    return outputdef train_neural_network(x):    prediction = neural_network_model(x)    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)    epochs = 20    with tf.Session() as sess:        sess.run(tf.global_variables_initializer()) #初始化变量        for epoch in range(epochs):            i = 0            while i < len(train_x):                #按批进行训练                start = i                end = i + batch_size                batch_x = np.array(train_x[start:end])                batch_y = np.array(train_y[start:end])                train = sess.run(optimizer,feed_dict={x: batch_x, y: batch_y})                c = sess.run(cost, feed_dict={x: batch_x, y: batch_y})                i += batch_size            cost_plt.append(c)            epoch_plt.append(epoch+1)            print('Epoch', epoch + 1, 'loss:', c)            #比较预测值和实际值是否相同,1表示按照 1轴方向            correct = tf.equal(tf.arg_max(prediction, 1), tf.argmax(y, 1))            #将布尔值转为float类型后求平均            accuracy = tf.reduce_mean(tf.cast(correct, 'float'))            accuracy = sess.run(accuracy,feed_dict={x: test_x, y: test_y})            accuracy_plt.append(accuracy)            print('Accuracy:',accuracy)        plt.plot(epoch_plt,accuracy_plt,'r',lw=2,label="accuracy")        plt.grid(True)        plt.legend()        plt.show()        plt.plot(epoch_plt,cost_plt,'c',lw=2,label="cost")        plt.grid(True)        plt.legend()        plt.show()train_neural_network(x)

结果

检测精度很低，效果不好，可能需要加大数据集后续考虑采用别的算法实验