bp神经网络python代码实现

BP神经网络的Python代码实现可以使用Pytorch库来实现，以下是一个简单的示例代码：

import torch
import torch.nn as nn

# 定义一个简单的BP神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(4, 8)  # 输入层到隐藏层
        self.fc2 = nn.Linear(8, 1)  # 隐藏层到输出层

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

# 创建模型和损失函数
net = Net()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

# 训练模型
inputs = torch.tensor([[1, 2, 3, 4]], dtype=torch.float32)
targets = torch.tensor([[0.5]], dtype=torch.float32)

for epoch in range(1000):
    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()

# 测试模型
test_input = torch.tensor([[2, 4, 6, 8]], dtype=torch.float32)
test_output = net(test_input)
print(test_output)