上代码：
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 #计算一共有多少个批次 #这里的none表示第一个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)) #初始化偏置值 biases = tf.Variable(tf.constant(0.1, shape=[n_classes])) #定义RNN网络 def RNN(X,weights,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[state,batch_size,cell.state_size] # final_state[0]是cell state # final_state[1]是hidden_state # outputs: The RNN output 'Tensor'. # If time_major == False (default), this will be a `Tensor` shaped: # `[batch_size, max_time, cell.output_size]`. # If time_major == True, this will be a `Tensor` shaped: # `[max_time, batch_size, cell.output_size]`. # final_state 记录的是最后一次的输出结果 # outputs 记录的是每一次的输出结果 outputs,final_state = tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32) results = tf.nn.softmax(tf.matmul(final_state[1],weights) + biases) 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_v2(logits=prediction,labels=y)) #使用AdamOptimizer进行优化 train_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 accuracy = 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(train_step,feed_dict={x:batch_xs,y:batch_ys}) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print ("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))
训练结果：
Iter 0, Testing Accuracy= 0.6474 Iter 1, Testing Accuracy= 0.8439 Iter 2, Testing Accuracy= 0.8876 Iter 3, Testing Accuracy= 0.9033 Iter 4, Testing Accuracy= 0.9039 Iter 5, Testing Accuracy= 0.9236