融入注意力机制的TCN网络结构代码

融入注意力机制的TCN（Temporal Convolutional Network）结构代码如下所示：

import torch
import torch.nn as nn

class Attention(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Attention, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.attention_weights = nn.Linear(input_size, hidden_size)
        self.attention_activation = nn.Tanh()
        self.attention_weights2 = nn.Linear(hidden_size, 1)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, inputs):
        attention_scores = self.attention_activation(self.attention_weights(inputs))
        attention_weights = self.attention_weights2(attention_scores)
        attention_weights = self.softmax(attention_weights)
        weighted_inputs = inputs * attention_weights
        output = torch.sum(weighted_inputs, dim=1)
        return output

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.tcn_layers = nn.ModuleList()
        for i, num_channel in enumerate(num_channels):
            dilation_size = 2 ** i
            tcn_layer = nn.Sequential(
                nn.Conv1d(input_size, num_channel, kernel_size, dilation=dilation_size),
                nn.BatchNorm1d(num_channel),
                nn.ReLU(),
                nn.Dropout(dropout)
            )
            self.tcn_layers.append(tcn_layer)
            input_size = num_channel

        self.attention = Attention(sum(num_channels), sum(num_channels) // 2)
        self.fc = nn.Linear(sum(num_channels), output_size)

    def forward(self, inputs):
        inputs = inputs.permute(0, 2, 1)
        outputs = inputs
        for tcn_layer in self.tcn_layers:
            outputs = tcn_layer(outputs)
        outputs = outputs.permute(0, 2, 1)
        attention_output = self.attention(outputs)
        output = self.fc(attention_output)
        return output

这段代码实现了一个融入注意力机制的TCN网络结构。其中，Attention模块用于计算输入序列的注意力权重，TCN模块通过多个卷积层和注意力层来提取特征并进行预测。你可以根据需要调整网络的输入大小、输出大小、通道数、卷积核大小和dropout率等参数。