# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据
mnist=input_data.read_data_sets("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)

#二次代价函数
# loss=tf.reduce_mean(tf.square(y-prediction))
#交叉熵损失函数
# cross_entropy=-tf.reduce_mean(y*tf.log(prediction))
#softmax搭配交叉熵
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

#使用梯度下降
train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init=tf.global_variables_initializer()

#结果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:
    sess.run(init)
    for _ in range(21):
        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})
        #用测试集的数据进行测试，得到准确率
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("epoch "+str(_)+",Testting Accuracy: "+str(acc))