# How to perform model weight regularization in PyTorch?

In PyTorch, you can use the parameters() method in the torch.nn.Module class to access the weight parameters of a model, and then apply regularization techniques to constrain these parameters. Below is an example code demonstrating how to apply L2 regularization to a model’s weights.

```
import torch
import torch.nn as nn
import torch.optim as optim
# 定义一个简单的神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 5)
self.fc2 = nn.Linear(5, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 创建模型实例
model = Net()
# 定义L2正则化参数
l2_lambda = 0.01
# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()
# 训练模型
for epoch in range(100):
optimizer.zero_grad()
# 正向传播
output = model(torch.randn(1, 10))
loss = criterion(output, torch.randn(1, 1))
# 添加L2正则化项
l2_reg = torch.tensor(0.)
for param in model.parameters():
l2_reg += torch.norm(param)
loss += l2_lambda * l2_reg
# 反向传播
loss.backward()
optimizer.step()
```

In the above example, we first defined a simple neural network model called Net, and then created an instance of the model. In the training loop, we used optimizer.zero_grad() to clear the previous gradients, followed by forward propagation and loss calculation. Next, we computed the L2 norm of all weight parameters and added it to the loss function as a regularization term. Finally, we performed backpropagation and updated the model parameters.