class LSTMMain(nn.Module): def __init__(self, input_size, output_len, lstm_hidden, lstm_layers, batch_size, device="cpu"): super(LSTMMain, self).__init__() self.lstm_hidden = lstm_hidden self.lstm_layers = lstm_layers self.lstmunit = LSTM(input_size, lstm_hidden, lstm_layers, batch_size, device) self.linear = nn.Linear(lstm_hidden, output_len) def forward(self, input_seq): ula, h_out = self.lstmunit(input_seq) out = ula.contiguous().view(ula.shape[0] * ula.shape[1], self.lstm_hidden) out = self.linear(out) out = out.view(ula.shape[0], ula.shape[1], -1) out = out[:, -1, :] return out根据这段代码 写一个bp神经网络的代码

好的，以下是一个简单的BP神经网络的代码示例：

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

class BPNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(BPNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        x = torch.sigmoid(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义模型和损失函数
input_size = 10
hidden_size = 20
output_size = 1
model = BPNet(input_size, hidden_size, output_size)
criterion = nn.MSELoss()

# 定义优化器
lr = 0.01
optimizer = optim.SGD(model.parameters(), lr=lr)

# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    # 前向传播和计算损失
    inputs = torch.randn(1, input_size) # 随机生成一个样本
    targets = torch.randn(1, output_size) # 随机生成一个目标值
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # 反向传播和更新参数
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    # 打印损失
    if (epoch+1) % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

这个代码示例定义了一个包含一个隐藏层的BP神经网络，输入大小为10，隐藏层大小为20，输出大小为1。使用均方误差作为损失函数，使用随机梯度下降作为优化器。然后进行了1000个epoch的训练，并打印了每100个epoch的损失值。