pytorch实现CNNL1正则化代码

下面是一个使用PyTorch实现CNN L1正则化的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5)
        self.fc1 = nn.Linear(32 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 32 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = CNN()

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        # 设置L1正则化项
        l1_regularization = torch.tensor(0)
        for param in model.parameters():
            l1_regularization += torch.norm(param, 1)
        l1_regularization *= 0.001

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels) + l1_regularization
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:  
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

在上述代码中，我们使用了`torch.norm`函数来计算参数的L1范数，并将其乘以一个较小的系数作为L1正则化项，加入到损失函数中。通过调整这个系数的大小，我们可以控制正则化的强度。