torch 残差网络

torch 残差网络（Residual Networks）是一种深度神经网络结构，通过引入残差块和跳跃连接的方式，解决了传统神经网络中梯度消失的问题，使得网络可以更深层次地进行训练。在 PyTorch 中，可以通过 nn.Module 实现残差网络的搭建，同时也可以使用预训练好的 ResNet 模型进行迁移学习。如果需要自己实现残差神经网络，可以参考引用中的代码实现。

相关问题

用torch写一个残差神经网络代码

以下是使用 Torch 实现的残差神经网络代码：

require 'nn'

function residualBlock(nChannels)
  local convModule = nn.Sequential()
    :add(nn.SpatialConvolution(nChannels, nChannels, 3,3, 1,1, 1,1))
    :add(nn.SpatialBatchNormalization(nChannels))
    :add(nn.ReLU(true))
    :add(nn.SpatialConvolution(nChannels, nChannels, 3,3, 1,1, 1,1))
    :add(nn.SpatialBatchNormalization(nChannels))
  return nn.Sequential()
    :add(nn.ConcatTable()
      :add(convModule)
      :add(nn.Identity()))
    :add(nn.CAddTable())
    :add(nn.ReLU(true))
end

function resNet(nChannels, nClasses, nResiduals)
  local nets = nn.Sequential()
  nets:add(nn.SpatialConvolution(3, nChannels, 3,3, 1,1, 1,1))
  nets:add(nn.SpatialBatchNormalization(nChannels))
  nets:add(nn.ReLU(true))
  for i=1,nResiduals do
    nets:add(residualBlock(nChannels))
  end
  nets:add(nn.SpatialAveragePooling(8,8))
  nets:add(nn.View(nChannels))
  nets:add(nn.Linear(nChannels, nClasses))
  nets:add(nn.LogSoftMax())
  return nets
end

model = resNet(64, 10, 5) -- 5 residual blocks

希望这个代码能够帮到您！

用python的torch写一个残差神经网络代码

以下是用 Python 的 PyTorch 编写残差神经网络的代码示例：

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, 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
        self.in_channels = in_channels
        self.out_channels = out_channels
        if self.stride != 1 or self.in_channels != self.out_channels:
            self.downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, stride=self.stride, bias=False),
                nn.BatchNorm2d(self.out_channels)
            )
        else:
            self.downsample = None

    def forward(self, x):
        residual = 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:
            residual = self.downsample(residual)
        out += residual
        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, self.in_channels, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channels)
        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):
        shortcut = nn.Sequential(
            nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels * block.expansion)
        )
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, shortcut))
        self.in_channels = out_channels * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_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

def ResNet50(num_classes):
    return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes=num_classes)

该代码实现了 ResNet50 神经网络，您可以更改参数来实现不同层数和其他类型的 ResNet 网络。