python 训练好的模型怎么保存_MNIST数据集训练完如何保存成模型文件？

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import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

# In[12]:

#载入数据集

mnist = input_data.read_data_sets("E://YangBen//MNIST_data",one_hot=True)

#每个批次的大小

batch_size = 100

#计算一共有多少个批次

n_batch = mnist.train.num_examples // batch_size

#定义两个placeholder

x = tf.placeholder(tf.float32,[None,784])

y = tf.placeholder(tf.float32,[None,10])

#创建一个简单的神经网络

W = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.zeros([10]))

prediction = tf.nn.softmax(tf.matmul(x,W)+b)

# ///隐藏层//

w1 = tf.Variable(tf.truncated_normal([784, 500], stddev=0.1))

b1 = tf.Variable(tf.zeros([500]))

L1 = tf.nn.relu(tf.matmul(x, w1) + b1)

w2 = tf.Variable(tf.truncated_normal([500, 300], stddev=0.1))

b2 = tf.Variable(tf.zeros([300]))

L2 = tf.nn.relu(tf.matmul(L1, w2) + b2)

# ///隐藏层//

# 输出层

w3 = tf.Variable(tf.truncated_normal([300, 10], stddev=0.1))

b3 = tf.Variable(tf.zeros([10]))

prediction = tf.nn.softmax(tf.matmul(L2, w3)+b3)

#二次代价函数

relu = tf.reduce_mean(tf.square(y-prediction))

#使用梯度下降法

train_step = tf.train.GradientDescentOptimizer(0.2).minimize(relu)

#初始化变量

init = tf.global_variables_initializer()

#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))

with tf.Session() as sess:

sess.run(init)

for epoch in range(5):

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))

这是我的代码，中间加了Saver之后还是报错