测试代码

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_example // batch_size

#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])#输入图像
x = tf.placeholder(tf.float32,[None,10])#输入标签

#创建一个简单的神经网络
W = tf.Variable(tf.zeros([784,10]))#生成784行，10列的全0矩阵
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))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

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

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