MNIST手写数字分类simple版（03-2）

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)

#每个批次的大小
batch_size=100
#计算一共有多少批次
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([1,10]))
prediction=tf.nn.softmax(tf.matmul(x,W)+b)

#二次代价函数
loss=tf.reduce_mean(tf.square(y-prediction))
#使用剃度下降法
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))

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(20):
        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))