TCN的Python代码

以下是一个简单的TCN（Temporal Convolutional Network）的Python代码示例：

import torch
import torch.nn as nn

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.num_channels = num_channels
        self.layers = len(num_channels)
        self.tcn = nn.ModuleList([])
        for i in range(self.layers):
            stride = 1
            padding = (kernel_size-1)//2
            dilation = 1
            if i == self.layers - 1:
                stride = 1
                padding = 0
                dilation = 1
            self.tcn.append(nn.Sequential(
                nn.Conv1d(num_channels[i-1] if i > 0 else input_size, num_channels[i],
                          kernel_size, stride=stride, padding=padding, dilation=dilation),
                nn.BatchNorm1d(num_channels[i]),
                nn.ReLU(),
                nn.Dropout(dropout)
            ))
        self.linear = nn.Linear(num_channels[-1], output_size)
        
    def forward(self, x):
        y = x.permute(0, 2, 1)
        for i in range(self.layers):
            y = self.tcn[i](y)
        y = y.permute(0, 2, 1)
        y = self.linear(y[:, -1, :])
        return y

这是一个基本的TCN模型，使用了卷积层、批归一化、ReLU激活函数和Dropout层。它将输入的维度从（batch_size，sequence_length，input_size）转换为（batch_size，output_size）。你可以根据需要进行修改和扩展。