Resnet神经网络

ResNet（残差网络）是一种深度卷积神经网络，由微软研究院于2015年提出。它的主要特点是引入了残差块（Residual Block），通过跳跃连接（skip connection）来解决深层网络的梯度消失和梯度爆炸问题，从而使得更深的网络结构可以训练得更好。

在传统的卷积神经网络中，每个层的输入都是通过非线性变换得到的，这可能导致信息的丢失。而在ResNet中，每个残差块的输入不仅仅是前一层的输出，还包括了前一层的输入。这样，网络可以学习到残差函数，即前一层的输出与输入之间的差异，从而更好地捕捉到特征。

下面是一个简单的ResNet示例：

import torch
import torch.nn as nn

# 定义残差块
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, 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)
        self.stride = stride

    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.stride != 1 or identity.size(1) != out.size(1):
            identity = nn.Conv2d(identity.size(1), out.size(1), kernel_size=1, stride=self.stride, bias=False)(identity)
            identity = nn.BatchNorm2d(out.size(1))(identity)

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

        return out

# 定义ResNet模型
class ResNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet, 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, 64, 3)
        self.layer2 = self._make_layer(64, 128, 4, stride=2)
        self.layer3 = self._make_layer(128, 256, 6, stride=2)
        self.layer4 = self._make_layer(256, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):
        layers = []
        layers.append(ResidualBlock(in_channels, out_channels, stride))
        for _ in range(1, num_blocks):
            layers.append(ResidualBlock(out_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

# 创建ResNet模型实例
model = ResNet()

# 打印模型结构
print(model)

这是一个简单的ResNet模型，包含了多个残差块和池化层。你可以根据自己的需求进行修改和扩展。