残差收缩网络pytorch

残差收缩网络（ResNet）是一种深度学习模型，它通过引入残差块来解决深度神经网络中梯度消失或爆炸的问题。在PyTorch中，ResNet可以通过torchvision.models.resnet模块来实现。

ResNet的核心思想是在网络中引入残差块，即将输入和输出进行相加，使得网络可以通过简单的恒等映射来学习残差。这样的做法可以使得网络更容易训练，同时也可以提高模型的准确率。ResNet中的残差块包括一个跨层连接和两个卷积层，其中跨层连接将输入直接加到输出上。

在PyTorch中，可以使用torchvision.models.resnet来实现ResNet网络。该模块提供了多种不同深度的ResNet模型，可以根据具体应用场景进行选择。同时，该模块还提供了预训练好的ResNet模型，可以直接使用或者在其基础上进行微调。

相关问题

写一段7分类的深度残差收缩网络pytorch代码

import torch
import torch.nn as nn# 定义残差收缩块
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, 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, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out += self.shortcut(x)
return out# 定义深度残差收缩网络
class ResNet(nn.Module):
def __init__(self, num_classes=7):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
self.avg_pool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512, num_classes) def make_layer(self, block, channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, channels))
self.in_channels = channels
return nn.Sequential(*layers) def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out# 构建7分类的深度残差收缩网络
resnet = ResNet(7)

深度残差收缩网络的pytorch代码

以下是深度残差收缩网络的 PyTorch 代码：

import torch
import torch.nn as nn

class ResidualShrinkageBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, padding=1, dilation=1, 
                 reduction_ratio=16, last=False):
        super(ResidualShrinkageBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, 
                               padding=padding, dilation=dilation, 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=padding, dilation=dilation, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.last = last
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(out_channels, out_channels // reduction_ratio)
        self.fc2 = nn.Linear(out_channels // reduction_ratio, out_channels)
        self.sigmoid = nn.Sigmoid()

    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.last:
            out = out
        else:
            out = self.avg_pool(out)
            out = out.view(out.size(0), -1)
            out = self.fc1(out)
            out = self.relu(out)
            out = self.fc2(out)
            out = self.sigmoid(out)
            out = out.view(out.size(0), out.size(1), 1, 1)
            out = out * identity

        out += identity
        out = self.relu(out)

        return out

class ResidualShrinkageNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResidualShrinkageNet, self).__init__()
        self.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(64, 256, 3)
        self.layer2 = self._make_layer(256, 512, 4, stride=2)
        self.layer3 = self._make_layer(512, 1024, 6, stride=2)
        self.layer4 = self._make_layer(1024, 2048, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(2048, num_classes)

    def _make_layer(self, in_channels, out_channels, blocks, stride=1):
        layers = []
        layers.append(ResidualShrinkageBlock(in_channels, out_channels, 
                                              stride=stride, last=True))
        for i in range(1, blocks):
            layers.append(ResidualShrinkageBlock(out_channels, out_channels))
        return nn.Sequential(*layers)

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

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

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

这是一个四层残差结构的 Residual Shrinkage Net，其中每个残差块有一个特殊的缩减层，用于减少冗余特征。可以根据需要调整层数和通道数。