tensorflow速度复习-网络结构

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

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

with tf.name_scope('layer'):
    #创建一个简单的神经网络：784-10
    with tf.name_scope('weights'):
        W=tf.Variable(tf.truncated_normal([784,10],stddev=0.1))
    with tf.name_scope('biases'):
        b=tf.Variable(tf.zeros([10])+0.1)
    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.losses.mean_squared_error(y,prediction)
with tf.name_scope('train'):
    #使用梯度下降法
    train=tf.train.GradientDescentOptimizer(0.3).minimize(loss)

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        #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))
    with tf.name_scope('accuracy'):
        #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())
    writer=tf.summary.FileWriter('logs/',sess.graph)
    #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 Accuuracy "+str(acc))