【知识---ResNet（Residual Network）作用及代码】

本文主要是介绍【知识---ResNet（Residual Network）作用及代码】，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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文章目录

前言
ResNet（Residual Network）作用
ResNet残差块的示例：
- tensorflow 版本
- pytorch 版本
总结

前言

提示：这里可以添加本文要记录的大概内容：

BN（批量归一化）可以解决网络层数太深而出现的梯度消失问题，但是如果网络层数太多，这个方法也是不太管用的。所以就提出了resnet，下面对该网络作进一步学习：

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ResNet（Residual Network）作用

ResNet（Residual Network）是由Microsoft Research提出的一种深度神经网络架构，

其主要特点是使用了残差块（Residual Blocks），通过跳跃连接（skip connection）来解决深度神经网络训练过程中的梯度消失和梯度爆炸问题。

这种结构使得网络可以更容易地学习恒等映射，从而更轻松地训练非常深的网络。

ResNet的基本构建块是残差块，其中输入通过跳跃连接直接添加到输出。

这样的设计有助于信息的流动，避免了梯度在网络中传递时过度减小，从而使得训练更加稳定。

ResNet残差块的示例：

tensorflow 版本

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, ReLU, Adddef residual_block(x, filters, kernel_size=3, stride=1):# 主要路径y = Conv2D(filters, kernel_size=kernel_size, strides=stride, padding='same')(x)y = BatchNormalization()(y)y = ReLU()(y)y = Conv2D(filters, kernel_size=kernel_size, padding='same')(y)y = BatchNormalization()(y)# 跳跃连接if stride != 1 or x.shape[-1] != filters:x = Conv2D(filters, kernel_size=1, strides=stride, padding='same')(x)# 相加操作out = Add()([x, y])out = ReLU()(out)return out# 构建简单的ResNet模型
input_tensor = Input(shape=(224, 224, 3))
x = Conv2D(64, kernel_size=7, strides=2, padding='same')(input_tensor)
x = BatchNormalization()(x)
x = ReLU()(x)
x = residual_block(x, filters=64)
x = residual_block(x, filters=64)
x = residual_block(x, filters=128, stride=2)
x = residual_block(x, filters=128)
x = residual_block(x, filters=256, stride=2)
x = residual_block(x, filters=256)
x = residual_block(x, filters=512, stride=2)
x = residual_block(x, filters=512)
output = tf.keras.layers.GlobalAveragePooling2D()(x)model = tf.keras.Model(inputs=input_tensor, outputs=output)model.summary()

上述代码中的residual_block函数定义了一个简单的残差块，而模型则是通过堆叠这些残差块来构建的。

这只是一个简单的ResNet模型示例，实际中可能会使用更深的网络。

pytorch 版本

import torch
import torch.nn as nn
import torch.nn.functional as Fclass BasicBlock(nn.Module):def __init__(self, in_channels, out_channels, stride=1):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.relu = nn.ReLU(inplace=True)self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(out_channels)# 跳跃连接self.shortcut = nn.Sequential()if stride != 1 or in_channels != out_channels:self.shortcut = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(out_channels))def forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out += self.shortcut(residual)out = self.relu(out)return outclass ResNet(nn.Module):def __init__(self, block, layers, num_classes=1000):super(ResNet, self).__init__()self.in_channels = 64self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)self.bn1 = nn.BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, layers[0])self.layer2 = self._make_layer(block, 128, layers[1], stride=2)self.layer3 = self._make_layer(block, 256, layers[2], stride=2)self.layer4 = self._make_layer(block, 512, layers[3], stride=2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512, num_classes)def _make_layer(self, block, out_channels, blocks, stride=1):layers = []layers.append(block(self.in_channels, out_channels, stride))self.in_channels = out_channelsfor _ in range(