利用tensorflow构建卷积神经网络（CNN）

学习前言

从网站建设到定制行业解决方案，为提供网站制作、成都网站设计服务体系，各种行业企业客户提供网站建设解决方案，助力业务快速发展。成都创新互联将不断加快创新步伐，提供优质的建站服务。

学习神经网络已经有一段时间，从普通的BP神经网络到LSTM长短期记忆网络都有一定的了解，但是从未系统的把整个神经网络的结构记录下来，我相信这些小记录可以帮助我更加深刻的理解神经网络。

简介

卷积神经网络(Convolutional Neural Networks, CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(Feedforward Neural Networks)，是深度学习(deep learning)的代表算法之一。

其主要结构分为输入层、隐含层、输出层。

在tensorboard中，其结构如图所示：

对于卷积神经网络而言，其输入层、输出层与平常的卷积神经网络无异。但其隐含层可以分为三个部分，分别是卷积层(对输入数据进行特征提取)、池化层(特征选择和信息过滤)、全连接层(等价于传统前馈神经网络中的隐含层)。

隐含层介绍

1、卷积层

卷积将输入图像放进一组卷积滤波器，每个滤波器激活图像中的某些特征。

假设一副黑白图像为5*5的大小，像这样：

利用如下卷积器进行卷积：

卷积结果为：

卷积过程可以提取特征，卷积神经网络是根据特征来完成分类的。

在tensorflow中，卷积层的重要函数是：

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)，其中：

1、input是输入量，shape是[batch, height, width, channels]。;

2、filter是使用的卷积核;

3、strides是步长，其格式[1,step,step,1]，step指的是在图像卷积的每一维的步长;

4、padding：string类型的量，只能是"SAME","VALID"其中之一，SAME表示卷积前后图像面积不变。

2、池化层

池化层用于在卷积层进行特征提取后，输出的特征图会被传递至池化层进行特征选择和信息过滤。

常见的池化是最大池化，最大池化指的是取出这些被卷积后的数据的最大值，就是取出其最大特征。

假设其池化窗口为2X2，步长为2。

原图像为：

池化后为：

在tensorflow中，池化层的重要函数是：

tf.nn.max_pool(value, ksize, strides, padding, data_format, name)

1、value：池化层的输入，一般池化层接在卷积层后面，shape是[batch, height, width, channels]。

2、ksize：池化窗口的大小，取一个四维向量，一般是[1, in_height, in_width, 1]。

3、strides：和卷积类似，窗口在每一个维度上滑动的步长，也是[1, stride,stride, 1]。

4、padding：和卷积类似，可以取’VALID’ 或者’SAME’。

这是tensorboard中卷积层和池化层的连接结构：

3、全连接层

全连接层与普通神经网络的结构相同，如图所示：

具体实现代码

卷积层、池化层与全连接层实现代码

def conv2d(x,W,step,pad): #用于进行卷积，x为输入值，w为卷积核

return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)

def max_pool_2X2(x,step,pad): #用于池化，x为输入值，step为步数

return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)

def weight_variable(shape): #用于获得W

initial = tf.truncated_normal(shape,stddev = 0.1) #从截断的正态分布中输出随机值

return tf.Variable(initial)

def bias_variable(shape): #获得bias

initial = tf.constant(0.1,shape=shape) #生成普通值

return tf.Variable(initial)

def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):

#用于添加全连接层

layer_name = 'layer_%s'%n_layer

with tf.name_scope(layer_name):

with tf.name_scope("Weights"):

Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")

tf.summary.histogram(layer_name+"/weights",Weights)

with tf.name_scope("biases"):

biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")

tf.summary.histogram(layer_name+"/biases",biases)

with tf.name_scope("Wx_plus_b"):

Wx_plus_b = tf.matmul(inputs,Weights) + biases

tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)

if activation_function == None :

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

print(activation_function)

outputs = tf.nn.dropout(outputs,keep_prob)

tf.summary.histogram(layer_name+"/outputs",outputs)

return outputs

def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):

#用于生成卷积层和池化层

layer_name = 'layer_%s'%n_layer

with tf.name_scope(layer_name):

with tf.name_scope("Weights"):

W_conv = weight_variable([5,5,in_z_dim,out_z_dim])

with tf.name_scope("biases"):

b_conv = bias_variable([out_z_dim])

with tf.name_scope("conv"):

#卷积层

h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv)

with tf.name_scope("pooling"):

#池化层

h_pool = max_pool_2X2(h_conv, pool_step, padding)

return h_pool

全部代码

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data",one_hot = "true")

def conv2d(x,W,step,pad):

return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)

def max_pool_2X2(x,step,pad):

return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)

def weight_variable(shape):

initial = tf.truncated_normal(shape,stddev = 0.1) #从截断的正态分布中输出随机值

return tf.Variable(initial)

def bias_variable(shape):

initial = tf.constant(0.1,shape=shape) #生成普通值

return tf.Variable(initial)无锡人流医院 http://www.0510bhyy.com/

def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):

layer_name = 'layer_%s'%n_layer

with tf.name_scope(layer_name):

with tf.name_scope("Weights"):

Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")

tf.summary.histogram(layer_name+"/weights",Weights)

with tf.name_scope("biases"):

biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")

tf.summary.histogram(layer_name+"/biases",biases)

with tf.name_scope("Wx_plus_b"):

Wx_plus_b = tf.matmul(inputs,Weights) + biases

tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)

if activation_function == None :

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

print(activation_function)

outputs = tf.nn.dropout(outputs,keep_prob)

tf.summary.histogram(layer_name+"/outputs",outputs)

return outputs

def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):

layer_name = 'layer_%s'%n_layer

with tf.name_scope(layer_name):

with tf.name_scope("Weights"):

W_conv = weight_variable([5,5,in_z_dim,out_z_dim])

with tf.name_scope("biases"):

b_conv = bias_variable([out_z_dim])

with tf.name_scope("conv"):

h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv)

with tf.name_scope("pooling"):

h_pool = max_pool_2X2(h_conv, pool_step, padding)

return h_pool

def compute_accuracy(x_data,y_data):

global prediction

y_pre = sess.run(prediction,feed_dict={xs:x_data,keep_prob:1})

correct_prediction = tf.equal(tf.arg_max(y_data,1),tf.arg_max(y_pre,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

result = sess.run(accuracy,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:1})

return result

keep_prob = tf.placeholder(tf.float32)

xs = tf.placeholder(tf.float32,[None,784])

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

x_image = tf.reshape(xs,[-1,28,28,1])

h_pool1 = add_cnn_layer(x_image, in_z_dim = 1, out_z_dim = 32, n_layer = "cnn1",)

h_pool2 = add_cnn_layer(h_pool1, in_z_dim = 32, out_z_dim = 64, n_layer = "cnn2",)

h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])

h_fc1_drop = add_layer(h_pool2_flat, 7*7*64, 1024, "layer1", activation_function = tf.nn.relu, keep_prob = keep_prob)

prediction = add_layer(h_fc1_drop, 1024, 10, "layer2", activation_function = tf.nn.softmax, keep_prob = 1)

with tf.name_scope("loss"):

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys,logits = prediction),name = 'loss')

tf.summary.scalar("loss",loss)

train = tf.train.AdamOptimizer(1e-4).minimize(loss)

init = tf.initialize_all_variables()

merged = tf.summary.merge_all()

with tf.Session() as sess:

sess.run(init)

write = tf.summary.FileWriter("logs/",sess.graph)

for i in range(5000):

batch_xs,batch_ys = mnist.train.next_batch(100)

sess.run(train,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:0.5})

if i % 100 == 0:

print(compute_accuracy(mnist.test.images,mnist.test.labels))