## TensorFlow实现卷积神经网络CNN

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[ 所属分类 开发（python） | 发布者 店小二05 | 时间 | 作者 红领巾 ] 0人收藏点击收藏

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 9 22:01:46 2017

@author: marsjhao
"""

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

sess = tf.InteractiveSession()

def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1) #标准差为0.1的正态分布
return tf.Variable(initial)

def bias_variable(shape):
initial = tf.constant(0.1, shape=shape) #偏差初始化为0.1
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],

x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# -1代表先不考虑输入的图片例子多少这个维度，1是channel的数量
x_image = tf.reshape(x, [-1, 28, 28, 1])
keep_prob = tf.placeholder(tf.float32)

# 构建卷积层1
W_conv1 = weight_variable([5, 5, 1, 32]) # 卷积核5*5，1个channel，32个卷积核，形成32个featuremap
b_conv1 = bias_variable([32]) # 32个featuremap的偏置
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # 用relu非线性处理
h_pool1 = max_pool_2x2(h_conv1) # pooling池化

# 构建卷积层2
W_conv2 = weight_variable([5, 5, 32, 64]) # 注意这里channel值是32
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 构建全连接层1
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool3 = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool3, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 构建全连接层2
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.global_variables_initializer().run()

for i in range(20001):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_:batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g" %(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob:0.5})
print("test accuracy %g" %accuracy.eval(feed_dict={x: mnist.test.images,
y_: mnist.test.labels, keep_prob: 1.0}))

1. tf.nn.conv2d(x,W, strides=[1, 1, 1, 1], padding='SAME')
2. tf.nn.max_pool(x,ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
tf.nn.max_pool是TensorFlow中的最大池化函数，x是4-D的输入tensor shape=[batch, height, width, channels]，ksize参数表示池化窗口的大小，取一个4维向量，一般是[1, height, width, 1]，因为我们不想在batch和channels上做池化，所以这两个维度设为了1，strides与tf.nn.conv2d相同，strides=[1, 2, 2, 1]可以缩小图片尺寸。padding参数也参见tf.nn.conv2d。

tags: tf,卷积,nn,variable,conv2d,TensorFlow,池化,batch,max,strides,神经,fc1

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