import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.compat.v1.disable_eager_execution()

#载入数据集
mnist=input_data.read_data_sets("MNIST_data",one_hot=True)

#每个批次大小
batch_size=100
#计算一共有多少个批次
n_bath=mnist.train.num_examples // batch_size
print(n_bath)

with tf.name_scope(‘input‘):
    #定义两个placeholder
    x=tf.compat.v1.placeholder(tf.float32,[None,784],name=‘x-input‘)
    y=tf.compat.v1.placeholder(tf.float32,[None,10],name=‘y-input‘)


with tf.name_scope(‘layer‘):
    #创建一个简单的神经网络
    with tf.name_scope(‘wights‘):
        W=tf.Variable(tf.zeros([784,10]),name=‘W‘)
    with tf.name_scope(‘biases‘):
        b=tf.Variable(tf.zeros([10]),name=‘b‘)
    with tf.name_scope(‘wx_plus_b‘):
        wx_plus_b=tf.matmul(x,W)+b
    with tf.name_scope(‘softmax‘):
        prediction=tf.nn.softmax(wx_plus_b)


with tf.name_scope(‘loss‘):
    #二次代价函数
    loss=tf.reduce_mean(tf.square(y-prediction))

with tf.name_scope(‘train‘):
    #梯度下降
    train_step=tf.compat.v1.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init=tf.compat.v1.global_variables_initializer()

with tf.name_scope(‘accuracy‘):
    with tf.name_scope(‘correct_prediction‘):
        #结果存放在一个布尔型列表中
        #返回的是一系列的True或False argmax返回一维张量中最大的值所在的位置，对比两个最大位置是否一致
        correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
    with tf.name_scope(‘accuracy‘):
        #求准确率
        #cast：将布尔类型转换为float，将True为1.0，False为0，然后求平均值
        accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


with tf.compat.v1.Session() as sess:
    sess.run(init)
    writer=tf.compat.v1.summary.FileWriter(‘logs/‘,sess.graph)
    for epoch in range(1):
        for batch in range(n_bath):
            #获得一批次的数据，batch_xs为图片，batch_ys为图片标签
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            #进行训练
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        #训练完一遍后，测试下准确率的变化

        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter "+str(epoch)+",Testing Accuracy "+str(acc))