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tensorflow卷积神经Inception V3网络结构代码解析_python_

2023-05-26 325人已围观

简介 tensorflow卷积神经Inception V3网络结构代码解析_python_

前言

学习了Inception V3卷积神经网络，总结一下对Inception V3网络结构和主要代码的理解。

GoogLeNet对网络中的传统卷积层进行了修改，提出了被称为 Inception 的结构，用于增加网络深度和宽度，提高深度神经网络性能。从Inception V1到Inception V4有4个更新版本，每一版的网络在原来的基础上进行改进，提高网络性能。本文介绍Inception V3的网络结构和主要代码。

1 非Inception Module的普通卷积层

首先定义一个非Inception Module的普通卷积层函数inception_v3_base，输入参数inputs为图片数据的张量。第1个卷积层的输出通道数为32，卷积核尺寸为【3x3】，步长为2，padding模式是默认的VALID，第1个卷积层之后的张量尺寸变为(299-3)/2+1=149，即【149x149x32】。

后面的卷积层采用相同的形式，最后张量尺寸变为【35x35x192】。这几个普通的卷积层主要使用了3x3的小卷积核，小卷积核可以低成本的跨通道的对特征进行组合。

def inception_v3_base(inputs,scepe=None): with tf.variable_scope(scope,'InceptionV3',[inputs]): with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding='VALID'): # 149 x 149 x 32 net = slim.conv2d(inputs,32,[3,3],stride=2,scope='Conv2d_1a_3x3') # 147 x 147 x 32' net = slim.conv2d(net,32),[3,3],scope='Conv2d_2a_3x3') # 147 x 147 x 64 net = slim.conv2d(net,64,[3,3],padding='SAME',scope='Conv2d_2b_3x3') # 73 x 73 x 64 net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3') # 73 x 73 x 80 net = slim.conv2d(net, 80, [1, 1], scope= 'Conv2d_3b_1x1') # 71 x 71 x 192. net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3',reuse=tf.AUTO_REUSE) # 35 x 35 x 192 net = slim.max_pool2d(net, [3, 3], stride=2, scope= 'MaxPool_5a_3x3')

2 三个Inception模块组

接下来是三个连续的Inception模块组，每个模块组有多个Inception module组成。

下面是第1个Inception模块组，包含了3个类似的Inception module，分别是：Mixed_5b，Mixed_5c，Mixed_5d。第1个Inception module有4个分支，

第1个分支是输出通道为64的【1x1】卷积，

第2个分支是输出通道为48的【1x1】卷积，再连接输出通道为64的【5x5】卷积，

第3个分支是输出通道为64的【1x1】卷积，再连接2个输出通道为96的【3x3】卷积，

第4个分支是【3x3】的平均池化，再连接输出通道为32的【1x1】卷积。

最后用tf.concat将4个分支的输出合并在一起，输出通道之和为54+64+96+32=256，最后输出的张量尺寸为【35x35x256】。

第2个Inception module也有4个分支，与第1个模块类似，只是最后连接输出通道数为64的【1x1】卷积，最后输出的张量尺寸为【35x35x288】。

第3个模块与第2个模块一样。

 with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding='SAME'): # 35 x 35 x 256 end_point = 'Mixed_5b' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net,depth(64),[1,1],scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(32), [1, 1], scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) # 64+64+96+32=256 end_points[end_point] = net # 35 x 35 x 288 end_point = 'Mixed_5c' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1') branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],scope='Conv_1_0c_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(64), [1, 1],scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],scope='Conv2d_0c_3x3') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3],scope='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points[end_point] = net # 35 x 35 x 288 end_point = 'Mixed_5d' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],scope='Conv2d_0b_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],scope='Conv2d_0c_3x3') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points[end_point] = net

第2个Inception模块组包含了5个Inception module，分别是Mixed_6a，Mixed_6b，Mixed_6ac，Mixed_6d，Mixed_6e。

每个Inception module包含有多个分支，第1个Inception module的步长为2，因此图片尺寸被压缩，最后输出的张量尺寸为【17x17x768】。

第2个Inception module采用了Fractorization into small convolutions思想，串联了【1x7】和【7x1】卷积，最后也是将多个通道合并。

第3、4个Inception module与第2个类似，都是用来增加卷积和非线性变化，提炼特征。张量尺寸不变，多个module后仍旧是【17x17x768】。

 # 17 x 17 x 768. end_point = 'Mixed_6a' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,padding='VALID', scope='Conv2d_1a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],scope='Conv2d_0b_3x3') branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2,padding='VALID', scope='Conv2d_1a_1x1') with tf.variable_scope('Branch_2'): branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',scope='MaxPool_1a_3x3') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) # (35-3)/2+1=17 end_points[end_point] = net # 17 x 17 x 768. end_point = 'Mixed_6b' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],scope='Conv2d_0b_1x7') branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],scope='Conv2d_0c_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],scope='Conv2d_0b_7x1') branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],scope='Conv2d_0c_1x7') branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], scope='Conv2d_0d_7x1') branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],scope='Conv2d_0e_1x7') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points[end_point] = net print(net.shape) # 17 x 17 x 768. end_point = 'Mixed_6c' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): ranch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],scope='Conv2d_0b_1x7') branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],scope='Conv2d_0c_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],scope='Conv2d_0b_7x1') branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],scope='Conv2d_0c_1x7') branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],scope='Conv2d_0d_7x1') branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],scope='Conv2d_0e_1x7') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points[end_point] = net # 17 x 17 x 768. end_point = 'Mixed_6d' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7') branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1') branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7') branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1') branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], sco e='AvgPool_0a_3x3') branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],scope='Conv2d_0b_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points[end_point] = net # 17 x 17 x 768. end_point = 'Mixed_6e' with tf.variable_scope(end_point): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch
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tensorflow卷积神经Inception V3网络结构代码解析_python_

目录

前言

1 非Inception Module的普通卷积层

2 三个Inception模块组

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