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)

# 批次大小

batch_size = 64

# 计算一个周期一共有多少个批次

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:784-10

W = tf.Variable(tf.truncated_normal([784,10], stddev=0.1))

b = tf.Variable(tf.zeros([10]) + 0.1)

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

# 二次代价函数

loss = tf.losses.mean_squared_error(y, prediction)

# 使用梯度下降法

train = tf.train.GradientDescentOptimizer(0.3).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))

with tf.Session() as sess:

    # 变量初始化

    sess.run(tf.global_variables_initializer())

    # 周期epoch：所有数据训练一次，就是一个周期

    for epoch in range(21):

        for batch in range(n_batch):

            # 获取一个批次的数据和标签

            batch_xs,batch_ys = mnist.train.next_batch(batch_size)

            sess.run(train,feed_dict={x:batch_xs,y:batch_ys})

        # 每训练一个周期做一次测试

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

        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))