pytorch如何建立残差网络
时间: 2023-10-18 14:22:45 浏览: 36
PyTorch可以通过定义一个Residual块来建立残差网络。 Residual块可以由多个卷积层组成。 残差网络中的每个块包括两层卷积和一个跳跃连接。
以下是一个基本的Residual块的PyTorch代码:
```
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
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 x.size(1) != out.size(1):
identity = nn.Conv2d(x.size(1), out.size(1), kernel_size=1, stride=self.stride, bias=False)(x)
identity = nn.BatchNorm2d(out.size(1))(identity)
out += identity
out = self.relu(out)
return out
```
这个块的核心是`forward`方法,它接受输入张量并执行两个卷积操作,其结果与等同维度的跳跃连接相加,然后应用ReLU激活函数。
这就是如何在PyTorch中构建Residual网络的一个例子。