代码实现
# -*- coding: UTF-8 -*- import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) #输入的图片是28*28 n_inputs=28 #输入一行，一行有28个数据 max_time=28 #一共28行 lstm_size=100 #隐层单元 n_classes=10 #10个分类 batch_size=50 #每批次50个样本 n_batch=mnist.train.num_examples // batch_size #计算一共有多少批次 #这里的none表示第一维度可以是任意的长度 x=tf.placeholder(tf.float32,[None,784]) #正确的标签 y=tf.placeholder(tf.float32,[None,10]) #初始化权值 weights=tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1)) #初始化偏执值 biases=tf.Variable(tf.constant(0.1,shape=[n_classes])) #定义RNN网络 def RNN(X,weight,biases): #inputs=[batch_size,max_time,n_inputs] inputs=tf.reshape(X,[-1,max_time,n_inputs]) #定义LSTM基本CELL lstm_cell=tf.contrib.rnn.BasicLSTMCell(lstm_size) #final_state[0]是cell state #final_state[1]是hidden_state outputs,final_state=tf.nn.dynamic_rnn(lstm_cell, inputs,dtype=tf.float32) results=tf.nn.softmax(tf.matmul(final_state[1], weights) + biases)#最后神经网络的输出 return results #计算RNN的返回结果 prediction=RNN(x, weights, biases) #损失函数 cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) #使用AdamOptimizer进行优化 trian_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #结果存放在一个布尔型列表中 correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1)) #argmax返回一维张量中最大的值所在的位置 #求准确率 accuarcy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #把correct_prediction变为float32类型 #初始化 init=tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for epoch in range(6): for batch in range(n_batch): batch_xs,batch_ys=mnist.train.next_batch(batch_size) sess.run(trian_step, feed_dict={x:batch_xs,y:batch_ys}) acc=sess.run(accuarcy, feed_dict={x:mnist.test.images,y:mnist.test.labels}) print ("Iter "+str(epoch)+", Testing Accuarcy= " + str(acc))
运行结果：（此代码还可以进行优化）
