tensorflow入门学习笔记 8.交叉熵

二次代价函数

交叉熵

交叉熵代码如下

# -*- coding:utf-8 -*-
# -*- coding:utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据
mnist = input_data.read_data_sets("MNIST-data/", one_hot=True)

# 每个批次的大小
batch_size = 100

# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 定义占位符
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 创建神经网路
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x, W)+b)

#交叉熵
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

init = tf.global_variables_initializer()

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))    # 返回一维张量中最大的值所在位置
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 准确率

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))