pytorch Conv2d L2 正则化
时间: 2023-10-22 07:12:27 浏览: 50
PyTorch的Conv2d层默认不包含L2正则化,但可以通过添加权重衰减参数来实现L2正则化。具体方法是在定义优化器时,将weight_decay参数设置为一个非零值,代表权重衰减的强度。例如,以下代码展示了如何使用L2正则化来训练一个简单的卷积神经网络:
```
import torch
import torch.nn as nn
import torch.optim as optim
# 定义卷积神经网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义优化器并添加L2正则化
net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9, weight_decay=0.001)
# 训练网络
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = nn.CrossEntropyLoss()(outputs, labels)
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
```
在定义优化器时,将weight_decay参数设置为0.001,即可实现L2正则化。需要注意的是,权重衰减是一种常用的正则化方法,但并不是唯一的正则化方法。在深度学习中,还有其他正则化方法如L1正则化、Dropout等,可以根据具体情况选择合适的方法。