gcn-gru预测代码

以下是使用GCN-GRU模型进行预测的代码示例：

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

class GCNGRU(nn.Module):
    def __init__(self, num_features, num_classes):
        super(GCNGRU, self).__init__()
        self.conv1 = GCNConv(num_features, 64)
        self.conv2 = GCNConv(64, 32)
        self.gru = GRUConv(32, 32)
        self.fc = nn.Linear(32, num_classes)

    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = self.gru(x, edge_index)
        x = self.fc(x)
        return F.log_softmax(x, dim=1)

其中，`num_features`表示输入特征的维度，`num_classes`表示输出类别的数量。模型中使用了两层GCN层和一层GRU层，最后通过全连接层输出预测结果。