importinput_#print(hello)#载入数据集

2022-07-04

# coding: utf-8

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#print(“hello”)

#载入数据集

mnist = input_data.read_data_sets(“F:\TensorflowProject\MNIST_data”,one_hot=True)

#每个批次的大小，训练时一次100张放入神经网络中训练

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])

#0-9十个数字

y = tf.placeholder(tf.float32,[None,10])

#创建一个神经网络

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

#loss = tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=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()

#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))

#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(100):

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})

#测试准确率

acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})

print(“Iter: “+str(epoch)+” ,Testing Accuracy “+str(acc))

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