残差网络实现pytorch
时间: 2023-11-12 15:56:43 浏览: 44
可以使用PyTorch实现残差网络(ResNet)。下面是一个简单的例子:
```python
import torch
import torch.nn as nn
# 定义基本的残差块
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
# 定义残差网络
class ResNet(nn.Module):
def __init__(self, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self.make_layer(64, 3, stride=1)
self.layer2 = self.make_layer(128, 4, stride=2)
self.layer3 = self.make_layer(256, 6, stride=2)
self.layer4 = self.make_layer(512, 3, stride=2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def make_layer(self, out_channels, blocks, stride):
layers = []
layers.append(ResidualBlock(self.in_channels, out_channels, stride))
self.in_channels = out_channels
for _ in range(1, 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.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avg_pool(out)
out = torch.flatten(out, 1)
out = self.fc(out)
return out
# 创建ResNet模型
model = ResNet()
```
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