基于pytorch将TCN-bigru-attention与resnet融合

将TCN-BiGRU-Attention和ResNet融合可以通过以下步骤完成：

1. 导入所需的库和包：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

2. 定义TCN-BiGRU-Attention模型和ResNet模型：

class TCN_BiGRU_Attention(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN_BiGRU_Attention, self).__init__()
        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size, dropout=dropout)
        self.gru = nn.GRU(num_channels[-1], num_channels[-1], bidirectional=True)
        self.attention = nn.Linear(num_channels[-1] * 2, 1)
        self.fc = nn.Linear(num_channels[-1] * 2, output_size)

    def forward(self, inputs):
        x = self.tcn(inputs.permute(0, 2, 1)).permute(0, 2, 1)
        x, _ = self.gru(x)
        attention_weights = F.softmax(self.attention(x).squeeze(-1), dim=1)
        x = torch.sum(x * attention_weights.unsqueeze(-1), dim=1)
        x = self.fc(x)
        return x


class ResNet(nn.Module):
    def __init__(self, input_size, output_size, num_channels):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv1d(input_size, num_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm1d(num_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(num_channels, num_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm1d(num_channels)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc = nn.Linear(num_channels * 64, output_size)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, self.num_flat_features(x))
        x = self.fc(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

3. 定义TCN-BiGRU-Attention模型和ResNet模型的融合模型：

class TCN_ResNet(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN_ResNet, self).__init__()
        self.tcn_bigru_attention = TCN_BiGRU_Attention(input_size, output_size, num_channels, kernel_size, dropout)
        self.resnet = ResNet(input_size, output_size, num_channels)

    def forward(self, x):
        x1 = self.tcn_bigru_attention(x)
        x2 = self.resnet(x)
        x = x1 + x2
        return x

在这个融合模型中，我们将TCN-BiGRU-Attention模型和ResNet模型分别作为两个子模块，并在融合模型中将它们的输出进行相加，作为最终输出。

4. 实例化TCN_ResNet模型并进行训练、测试等操作。

model = TCN_ResNet(input_size, output_size, num_channels, kernel_size, dropout)

通过以上步骤的操作，即可将TCN-BiGRU-Attention模型和ResNet模型进行融合，获得更好的模型表现。