深度学习Week19——学习残差网络和ResNet50V2算法

文章目录
深度学习Week18——学习残差网络和ResNet50V2算法
一、前言
二、我的环境
三、论文解读
3.1 预激活设计
3.2 残差单元结构
四、模型复现
4.1 Residual Block
4.2 堆叠Residual Block
4.3. ResNet50V2架构复现

一、前言

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊 | 接辅导、项目定制

本周由于临近期末，被各种各样事情耽误，学习效果很差，但是仍要坚持打卡，展示自己的学校成果，或许我会选择休息一周，整理一下事情，再重新学习本周内容，因此本周主要是代码复现，更深层次的学习放在未来两周，包括数据集的验证、Pytorch复现代码。

二、我的环境

电脑系统：Windows 10
语言环境：Python 3.8.0
编译器：Pycharm2023.2.3
深度学习环境：TensorFlow
显卡及显存：RTX 3060 8G

三、论文解读

我花了一周的时间大致阅读了何恺明大佬的论文，由于时间问题，我只能给出我的理解，可能有错误，欢迎大家指正。

1、预激活设计

ResNet：采用传统的后激活设计，即批量归一化（Batch Normalization，简称BN）和ReLU激活函数位于卷积层之后。
ResNetV2：引入了预激活设计，将BN和ReLU移动到卷积层之前。这种设计被称为“Pre-Activation”，它改变了信息流和梯度流，有助于优化过程。
在这里插入图片描述
从上图中，我们可以很明显的看出原始残差单元、批量归一化后加法、加法前ReLU、仅ReLU预激活、完全预激活，何恺明大佬进行了4种新的尝试，可以看出最好的结果是(e)full pre-activation，其次到(a)original。
对于这个抽象的概念，我咨询了Kimi.ai，让他帮我解释，我试着理解（由于时间问题，本周很多内容都是请教AI的，但AI我觉得不一定准确，还需要小心求证）

原始方法：每个队员跑完后，我们会给他们一个鼓励的拍手（ReLU激活函数），让他们振奋精神，然后他们把接力棒交给下一个队员，并且下一个队员在接棒前会做一些热身运动（批量归一化，BN）。
改变后的第一种方法：这次我们让队员跑完后先做热身运动，然后再给他们拍手鼓励。这样队员们在接力时可能会有点混乱，表现不如原来好。
改变后的第二种方法：我们让队员在接棒前就给他们拍手鼓励，这样他们在跑的时候可能更有动力，但可能因为热身不充分，效果一般。
改变后的第三种方法：我们只给队员拍手鼓励，不做热身运动。这样队员们的表现和原来差不多，但可能因为没有热身，潜力没有完全发挥出来。
改变后的第四种方法：我们让队员在做热身运动和拍手鼓励之后再接棒。这样他们既做好了准备，又得到了鼓励，跑得更快，表现最好。

因此我们发现，预激活可以简化信息流并提高优化的容易度

2、残差单元结构

在咱们深度学习中，当我们增加网络的层数时，理论上网络的性能应该更好，因为有更多的数据可以用于学习复杂的特征。但实际情况是，过深的网络会变得难以训练，性能反而下降。残差单元因此诞生。

一个残差单元包Identity Path和Residual Function。
Identity Path就是将输入直接传递到单元的输出，不做任何处理，就像是一个"shortcut"或者“跳跃连接”。如下图，何恺明大佬提出了6种不同的shortcut在残差网络中的使用方式，以及它们是如何影响信息传递的
在这里插入图片描述
分别是原始，0，5倍缩放因子（减弱信息）c，d，e不理解和f应用dropout技术来随机丢弃一些信息，我觉得目的主要都是防止过拟合、增加模型效率，他们的结果如下：
最原始的(a)original 结构是最好的，也就是 identity mapping 恒等映射是最好的

四、模型复现

（这部分代码我由于最近事情太多就直接复制粘贴了，很不好，我会尽快改正！！）

官方调用

tf.keras.applications.resnet_v2.ResNet50V2(include_top=True,weights='imagenet',input_tensor=None,input_shape=None,pooling=None,classes=1000,classifier_activation='softmax'
)# ResNet50V2、ResNet101V2与ResNet152V2的搭建方式完全一样，区别在于堆叠Residual Block的数量不同。import tensorflow as tf
import tensorflow.keras.layers as layers
from tensorflow.keras.models import Model

4.1 Residual Block

"""
残差块Arguments:x: 输入张量filters: integer, filters of the bottleneck layer.kernel_size: 默认是3, kernel size of the bottleneck layer.stride: default 1, stride of the first layer.conv_shortcut: default False, use convolution shortcut if True,otherwise identity shortcut.name: string, block label.Returns:Output tensor for the residual block.
"""
def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):preact = layers.BatchNormalization(name=name + '_preact_bn')(x)preact = layers.Activation('relu', name=name + '_preact_relu')(preact)if conv_shortcut:shortcut = layers.Conv2D(4 * filters, 1, strides=stride, name=name + '_0_conv')(preact)else:shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else xx = layers.Conv2D(filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact)x = layers.BatchNormalization(name=name + '_1_bn')(x)x = layers.Activation('relu', name=name + '_1_relu')(x)x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)x = layers.Conv2D(filters,kernel_size,strides=stride,use_bias=False,name=name + '_2_conv')(x)x = layers.BatchNormalization(name=name + '_2_bn')(x)x = layers.Activation('relu', name=name + '_2_relu')(x)x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)x = layers.Add(name=name + '_out')([shortcut, x])return x

# ResNet50
if conv_shortcut:shortcut = layers.Conv2D(4 * filters, 1, strides=stride, name=name + '_0_conv')(x)shortcut = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
else:shortcut = x# ResNet50V2 区别还是很显然的if conv_shortcut:shortcut = layers.Conv2D(4 * filters, 1, strides=stride, name=name + '_0_conv')(preact)
else:# 注意后面还多了if语句shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x、

2. 堆叠Residual Block

def stack2(x, filters, blocks, stride1=2, name=None):x = block2(x, filters, conv_shortcut=True, name=name + '_block1')for i in range(2, blocks):x = block2(x, filters, name=name + '_block' + str(i))x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))return x

3. ResNet50V2架构复现

def ResNet50V2(include_top=True,  # 是否包含位于网络顶部的全连接层preact=True,  # 是否使用预激活use_bias=True,  # 是否对卷积层使用偏置weights='imagenet',input_tensor=None,  # 可选的keras张量，用作模型的图像输入input_shape=None,pooling=None,classes=1000,  # 用于分类图像的可选类数classifier_activation='softmax'):  # 分类层激活函数img_input = layers.Input(shape=input_shape)x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)if not preact:x = layers.BatchNormalization(name='conv1_bn')(x)x = layers.Activation('relu', name='conv1_relu')(x)x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)x = stack2(x, 64, 3, name='conv2')x = stack2(x, 128, 4, name='conv3')x = stack2(x, 256, 6, name='conv4')x = stack2(x, 512, 3, stride1=1, name='conv5')if preact:x = layers.BatchNormalization(name='post_bn')(x)x = layers.Activation('relu', name='post_relu')(x)if include_top:x = layers.GlobalAveragePooling2D(name='avg_pool')(x)x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x)else:if pooling == 'avg':# GlobalAveragePooling2D就是将每张图片的每个通道值各自加起来再求平均，# 最后结果是没有了宽高维度，只剩下个数与平均值两个维度。# 可以理解为变成了多张单像素图片。x = layers.GlobalAveragePooling2D(name='avg_pool')(x)elif pooling == 'max':x = layers.GlobalMaxPooling2D(name='max_pool')(x)model = Model(img_input, x)return model

if __name__ == '__main__':model = ResNet50V2(input_shape=(224, 224, 3))model.summary()

Model: "model"
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block1_2_pad[0][0]         
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv5_block1_preact_relu[0][0]   
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_conv[0][0]        conv5_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block1_out[0][0]           
__________________________________________________________________________________________________
conv5_block2_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block2_preact_bn[0][0]     
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block2_preact_relu[0][0]   
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block2_2_pad[0][0]         
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           conv5_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block2_out[0][0]           
__________________________________________________________________________________________________
conv5_block3_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block3_preact_bn[0][0]     
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block3_preact_relu[0][0]   
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block3_2_pad[0][0]         
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_out (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           conv5_block3_3_conv[0][0]        
__________________________________________________________________________________________________
post_bn (BatchNormalization)    (None, 7, 7, 2048)   8192        conv5_block3_out[0][0]           
__________________________________________________________________________________________________
post_relu (Activation)          (None, 7, 7, 2048)   0           post_bn[0][0]                    
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048)         0           post_relu[0][0]                  
__________________________________________________________________________________________________
predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   
==================================================================================================
Total params: 25,613,800
Trainable params: 25,568,360
Non-trainable params: 45,440