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)

#每个批次100张照片

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，输入层784个神经元，输出层10个神经元

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

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

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

#二次代价函数

# loss = tf.reduce_mean(tf.square(y-prediction))

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

#使用梯度下降法

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

#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))

saver = tf.train.Saver()

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))

    #保存模型

    saver.save(sess,'net/my_net.ckpt')

#载入数据集

mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

#每个批次100张照片

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，输入层784个神经元，输出层10个神经元

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

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

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

#二次代价函数

# loss = tf.reduce_mean(tf.square(y-prediction))

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

#使用梯度下降法

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

#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))

saver = tf.train.Saver()

with tf.Session() as sess:

    sess.run(init)

    print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))

    saver.restore(sess,'net/my_net.ckpt')

    print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))

#

0.098

INFO:tensorflow:Restoring parameters from net/my_net.ckpt

0.9179