# -*- coding: utf-8 -*-
"""
Created on Sat Jun 3 06:00:19 2017
@author: 代码医生 qq群：40016981，公众号：xiangyuejiqiren
@blog：http://blog.csdn.net/lijin6249
"""
import tensorflow as tf
# 导入 MINST 数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/data/", one_hot=True)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def avg_pool_7x7(x):
return tf.nn.avg_pool(x, ksize=[1, 7, 7, 1],
strides=[1, 7, 7, 1], padding='SAME')
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#########################################################new
W_conv3 = weight_variable([5, 5, 64, 10])
b_conv3 = bias_variable([10])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
nt_hpool3=avg_pool_7x7(h_conv3)#64
nt_hpool3_flat = tf.reshape(nt_hpool3, [-1, 10])
y_conv=tf.nn.softmax(nt_hpool3_flat)
cross_entropy = -tf.reduce_sum(y*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 启动session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(200):#20000
batch = mnist.train.next_batch(50)#50
if i%20 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y: batch[1]})
print( "step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y: batch[1]})
print ("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y: mnist.test.labels}))
