TCN-BIGRU代码

抱歉，我之前的回答有误。以下是关于TCN-BIGRU的 Python 代码实现示例：

import torch
import torch.nn as nn
from torch.nn.utils import weight_norm

class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding):
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation))
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.2)
        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation))
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.2)
        
        self.net = nn.Sequential(
            self.conv1,
            self.relu1,
            self.dropout1,
            self.conv2,
            self.relu2,
            self.dropout2
        )
        
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()
        
    def forward(self, x):
        out = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        return self.relu(out + res)

class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2 ** i
            in_channels = num_inputs if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size, padding=(kernel_size-1)*dilation_size)]
            
        self.network = nn.Sequential(*layers)
        
    def forward(self, x):
        return self.network(x)

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, tcn_kernel_size):
        super(TCN, self).__init__()
        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size=tcn_kernel_size)
        self.bigru = nn.GRU(num_channels[-1], hidden_size=num_channels[-1]//2, bidirectional=True)
        self.linear = nn.Linear(num_channels[-1]*2, output_size)
        
    def forward(self, x):
        tcn_out = self.tcn(x.transpose(1, 2)).transpose(1, 2)
        bigru_out, _ = self.bigru(tcn_out.permute(2, 0, 1))
        out = self.linear(bigru_out[-1])
        return out

其中，TCN是Temporal Convolutional Neural Network的缩写，BIGRU是 Bidirectional Gated Recurrent Unit 的缩写。