tcn pytorch实现

TCN（Temporal Convolutional Networks）是一种利用卷积神经网络处理时间序列数据的方法，它可以通过并行卷积层提取不同时间尺度的特征，并在最后一层中进行组合，从而达到更好的性能。以下是TCN在PyTorch中的实现示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.dropout = dropout
        self.layers = []
        self.num_levels = len(num_channels)
        
        # Dilated Convolutional Layers
        for i in range(self.num_levels):
            dilation_size = 2 ** i
            in_channels = self.input_size if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            self.layers += [DilatedConv1d(in_channels, out_channels, kernel_size, dilation=dilation_size)]
        
        # Residual Blocks and Dropout
        self.res_blocks = nn.Sequential(*self.layers)
        self.dropout = nn.Dropout(dropout)
        
        # Output Layer
        self.fc = nn.Linear(num_channels[-1], output_size)
    
    def forward(self, x):
        out = self.res_blocks(x)
        out = F.relu(out) # ReLU Activation
        out = F.avg_pool1d(out, kernel_size=out.size()[2]) # Global Average Pooling
        out = self.dropout(out)
        out = self.fc(out.squeeze())
        return out

class DilatedConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, dilation=1):
        super(DilatedConv1d, self).__init__()
        self.kernel_size = kernel_size
        self.dilation = dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation)
        self.padding = (kernel_size - 1) * dilation
    
    def forward(self, x):
        padding = x.size()[2] % self.kernel_size
        if padding != 0:
            padding = self.kernel_size - padding
        x = F.pad(x, (self.padding, 0))
        out = self.conv(x)
        return out