LSTM实现Mnist手写数字识别

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow.compat.v1 as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.compat.v1.disable_eager_execution()

# 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 = 64 #每批次64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
    lstm_cell = tf.nn.rnn_cell.LSTMCell(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]`.
    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
loss = tf.losses.softmax_cross_entropy(y,prediction)
# 使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-3).minimize(loss)
# 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))
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# 初始化
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(11):
        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))