bp神经网络预测模型l2正则化代码

下面是一个使用L2正则化的BP神经网络预测模型的代码示例（使用Python语言和PyTorch框架）：

import torch
import torch.nn as nn
import torch.optim as optim

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

net = Net()
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=0.01)

for epoch in range(100):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        l2_regularization = 0.0
        for param in net.parameters():
            l2_regularization += torch.norm(param, 2)
        loss += 0.01 * l2_regularization
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

这个代码示例中，我们定义了一个包含两个全连接层的神经网络模型，采用MSE损失函数和SGD优化器。在每次迭代中，我们对网络参数进行L2正则化，将L2范数乘以0.01加入到损失函数中，从而对网络参数进行约束。在训练过程中，我们通过计算L2范数来计算正则化项的值。