# -*- coding: utf-8 -*-
"""
Created on: 2017/10/29
@author : Shawn
function :
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data # 入口函数
if __name__ == '__main__': # 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 # 命名空间
with tf.name_scope('input'):
# 定义两个placeholder
x = tf.placeholder(tf.float32, [None, 784], name='x-input') # 输入层784个神经元
y = tf.placeholder(tf.float32, [None, 10], name='y-input') # 输出层10个神经元，10类 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))
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) # 0.2为学习率
train_step = tf.train.AdamOptimizer(1e-1).minimize(loss) # 初始化变量
init = tf.global_variables_initializer() # 结果存在一个bool类型的列表中
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction, 1)) # agmax返回一维张量中最大值所在的位置 # 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess:
sess.run(init)
writer = tf.summary.FileWriter('logs/', sess.graph) # 把所有图片训练21次
for epoch in range(1): # 训练n_batch批次
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)) pass
代码
进入cmd:
tensorboard –logdir=F:\PycharmProjects\TFlearn\src\logs
输出一个网址：
用google浏览器或者火狐打开
