GNN-LSTM 代码

以下是使用PyTorch实现的GNN-LSTM的代码示例，其中包括了GNN和LSTM的实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GNNLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, dropout):
        super(GNNLSTM, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_layers = num_layers
        self.dropout = dropout

        # GNN part
        self.conv1 = GCNConv(input_dim, hidden_dim)
        self.convs = nn.ModuleList()
        for i in range(num_layers - 1):
            self.convs.append(GCNConv(hidden_dim, hidden_dim))
        self.conv_last = GCNConv(hidden_dim, output_dim)

        # LSTM part
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True, dropout=dropout)

    def forward(self, x, edge_index, h, c):
        # GNN part
        x = F.relu(self.conv1(x, edge_index))
        for conv in self.convs:
            x = F.relu(conv(x, edge_index))
            x = F.dropout(x, self.dropout, training=self.training)
        x = self.conv_last(x, edge_index)

        # LSTM part
        x, (h, c) = self.lstm(x.unsqueeze(0), (h, c))
        x = x.squeeze(0)

        return x, h, c

在这个GNN-LSTM模型中，我们使用了一个GCNConv层来实现GNN的部分，使用了一个nn.LSTM层来实现LSTM的部分。在forward方法中，我们首先对输入进行GNN计算，然后将结果作为LSTM的输入，获得最终的输出。