pytorch构建bp神经网络

在PyTorch中构建BP神经网络的步骤如下：

1. 导入必要的库和模块：

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

2. 定义神经网络模型：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(输入层特征数, 隐藏层特征数)
        self.fc2 = nn.Linear(隐藏层特征数, 输出层特征数)

    def forward(self, x):
        x = torch.sigmoid(self.fc1(x))
        x = self.fc2(x)
        return x

3. 定义损失函数和优化器：

criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=学习率)

4. 训练模型：

for epoch in range(训练轮数):
    running_loss = 0.0
    for i, data in enumerate(训练集, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(训练集)))