tensorflow1.1/构建卷积神经网络识别文本

环境：tensorflow 1.1，python3

#coding:utf-8import numpy as npimport tensorflow as tfimport pickle#import matplotlib.pyplot as pltwith open('sentiment_set.pickle','rb') as f:        [test_data,test_labels,train_data,train_labels] = pickle.load(f)#shuffle datanp.random.seed(100)train_data = np.random.permutation(train_data)np.random.seed(100)train_labels = np.random.permutation(train_labels)np.random.seed(200)train_data = np.random.permutation(test_data)np.random.seed(200)train_labels = np.random.permutation(test_labels)batch_size = 120learning_rate = 0.01#词向量有423列xs = tf.placeholder(tf.float32,[None,423])ys = tf.placeholder(tf.int32,[None,2])#keep_prob = tf.placeholder(tf.float32)#传入卷积神经网络x = tf.reshape(xs,[-1,47,9,1])conv1 = tf.layers.conv2d(inputs=x,filters=3,kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)pool1 = tf.layers.max_pooling2d(conv1,pool_size=2,strides=2)conv2 = tf.layers.conv2d(pool1,filters=6,kernel_size=3,strides=1,padding='same',activation=tf.nn.relu)pool2 = tf.layers.max_pooling2d(conv2,pool_size=2,strides=2)flat = tf.reshape(pool2,[-1,2*11*6])dense = tf.layers.dense(flat,64)#dropout1 = tf.nn.dropout(dense,keep_prob)output = tf.layers.dense(dense,2)loss = tf.losses.softmax_cross_entropy(onehot_labels=ys,logits=output)train = tf.train.AdamOptimizer(learning_rate).minimize(loss)accuracy = tf.metrics.accuracy(labels=tf.argmax(ys,axis=1),predictions=tf.argmax(output,axis=1))[1]with tf.Session() as sess:    init = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())    sess.run(init)    for step in range(10000):        i = 0        while i < train_data.shape[0]:            batch_x = train_data[i:i+batch_size]            batch_y = train_labels[i:i+batch_size]            i = i+batch_size            _,c = sess.run([train,loss],feed_dict={xs:batch_x,ys:batch_y})        if step % 10 ==0:            acc = sess.run(accuracy,feed_dict={xs:test_data,ys:test_labels})            print('= = = = = => > > > > >','step:',int(step/10),'loss: %.4f' %c,'accuracy:%.2f' %acc)

结果：

采用卷积神经网络，精度有一定提升，但是提升不高，后续考虑采用word2vec+cnn处理文本分类问题