BP神经网络2个权重如何用衰减来正则化网络,matlab代码
时间: 2023-03-08 10:46:00 浏览: 85
可以使用L2正则化,即在损失函数中添加一个惩罚项,类似于:loss = loss + λ * (w1^2 + w2^2),其中λ为正则化系数,w1和w2分别为两个权重值。Matlab代码可以参考如下:net.performParam.regularization = 0.01;
net.performParam.normalization = 'none';
net = train(net, x, t);
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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):
```python
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。
bp神经网络预测模型l2正则化代码jupyter
以下是一个使用Jupyter Notebook展示的BP神经网络预测模型的L2正则化代码示例(使用Python语言和PyTorch框架):
```python
# 导入必要的库
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
# 生成数据
x = np.linspace(-1, 1, 100).reshape(-1, 1)
y = 3 * np.power(x, 2) + 0.5 + np.random.normal(0, 0.1, (100, 1))
# 将数据转换为Tensor类型
x_train = torch.from_numpy(x).float()
y_train = torch.from_numpy(y).float()
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(1, 10)
self.fc2 = nn.Linear(10, 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)
# 训练模型并进行L2正则化
for epoch in range(1000):
optimizer.zero_grad()
outputs = net(x_train)
loss = criterion(outputs, y_train)
l2_regularization = 0.0
for param in net.parameters():
l2_regularization += torch.norm(param, 2)
loss += 0.01 * l2_regularization
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print('Epoch %d loss: %.3f' % (epoch + 1, loss.item()))
# 绘制结果
plt.plot(x, y, 'ro', label='Original data')
plt.plot(x, net(x_train).data.numpy(), label='Fitted line')
plt.legend()
plt.show()
```
在这个代码示例中,我们首先生成了一些带有噪声的二次函数数据,并将其转换为Tensor类型。然后我们定义了一个包含两个全连接层的神经网络模型,并使用MSE损失函数和SGD优化器进行训练。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。最后,我们使用Matplotlib库绘制了原始数据和拟合曲线。