残差网络expansion

残差网络（ResNet）是一种深度神经网络结构，其通过引入残差块（Residual Block）来解决梯度消失和梯度爆炸的问题，从而实现更深层次的网络。残差块中包含了一个短路连接（shortcut connection），可以将输入直接传递到输出，避免了信息的丢失和变形。

残差网络的expansion指的是在残差块中增加了通道数，即将输入的通道数进行扩张（expand），然后再进行卷积计算。这个操作可以增加网络的表达能力，提高了模型的性能。在残差网络中，expansion通常被应用于第一个卷积层和最后一个卷积层，以及每个残差块中的第一个卷积层。这样可以增加网络的深度和宽度，提高模型的输入输出特征的丰富性，从而更好地适应各种数据集和任务。

相关问题

class BasicBlock(nn.Module):#浅层残差结构 expansion = 1

这段代码定义了一个名为`BasicBlock`的类，该类继承自`nn.Module`类。`BasicBlock`类表示一个浅层残差结构，用于组成深度残差网络中的基本模块。具体来说，浅层残差结构由两个卷积层、两个批归一化层、一个ReLU激活函数和一个残差连接组成。

`expansion=1`表示该模块的扩张系数为1，即不改变输入特征图的通道数。

需要注意的是，这段代码只定义了`BasicBlock`类的基本结构，具体的前向传播函数和权重初始化方式等需要在后续代码中进行定义。

残差网络代码pytorch

残差网络的代码在PyTorch中可以这样实现：

import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.stride = stride

        if stride != 1 or in_channels != out_channels:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),
                nn.BatchNorm2d(out_channels)
            )
        else:
            self.downsample = None
    
    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)
        
        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        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 * block.expansion, 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_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
        
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.maxpool(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)

        return out

def ResNet18():
    return ResNet(ResidualBlock, [2, 2, 2, 2])