帮我用python写一个用TCN进行多输入单输出预测的代码

好的，以下是一个使用TCN进行多输入单输出预测的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class MultiInputDataset(Dataset):
    def __init__(self, x1, x2, y):
        self.x1 = x1
        self.x2 = x2
        self.y = y

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        return self.x1[idx], self.x2[idx], self.y[idx]

class TCN(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TCN, self).__init__()
        self.tcn = nn.Sequential(
            nn.Conv1d(num_inputs, num_channels, kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Conv1d(num_channels, num_channels, kernel_size),
            nn.BatchNorm1d(num_channels),
            nn.ReLU(),
            nn.Dropout(dropout)
        )
        self.linear = nn.Linear(num_channels, 1)

    def forward(self, x1, x2):
        x = torch.cat((x1, x2), dim=1)
        x = self.tcn(x.transpose(1, 2)).transpose(1, 2)
        x = self.linear(x[:, :, -1])
        return x

# Generate some example data
x1 = torch.randn(100, 5, 10) # 100 samples, 5 features, 10 time steps
x2 = torch.randn(100, 3, 10) # 100 samples, 3 features, 10 time steps
y = torch.randn(100, 1) # 100 samples, 1 target value

# Create a dataset and dataloader
dataset = MultiInputDataset(x1, x2, y)
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)

# Create a TCN model
model = TCN(8, 16)

# Define the loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, (x1_batch, x2_batch, y_batch) in enumerate(dataloader):
        optimizer.zero_grad()
        y_pred = model(x1_batch, x2_batch)
        loss = criterion(y_pred, y_batch)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 10 == 9:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

这段代码实现了一个使用TCN进行多输入单输出预测的模型。其中，MultiInputDataset用于将多个输入数据组合成一个数据集，TCN定义了一个简单的TCN模型，可以接收多个输入数据。在训练过程中，我们使用MSE损失函数和Adam优化器来进行优化。