时序图卷积神经网络代码

以下是一个简单的时序图卷积神经网络（Temporal Graph Convolutional Network）的代码例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TGCNLayer(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(TGCNLayer, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.linear = nn.Linear(out_channels, out_channels)

    def forward(self, x, A):
        x = self.conv(x)
        x = torch.matmul(A, x)  # A为时序图的邻接矩阵
        x = self.linear(x)
        x = F.relu(x)
        return x

class TGCN(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super(TGCN, self).__init__()
        self.layer1 = TGCNLayer(in_channels, hidden_channels)
        self.layer2 = TGCNLayer(hidden_channels, out_channels)

    def forward(self, x, A):
        x = self.layer1(x, A)
        x = self.layer2(x, A)
        return x

# 使用示例
in_channels = 3  # 输入特征的通道数
hidden_channels = 16  # 隐层特征的通道数
out_channels = 64  # 输出特征的通道数
num_nodes = 10  # 时序图的节点数

x = torch.randn(1, in_channels, num_nodes, num_nodes)  # 输入特征
A = torch.randn(1, num_nodes, num_nodes)  # 邻接矩阵

model = TGCN(in_channels, hidden_channels, out_channels)
output = model(x, A)

请注意，这只是一个简单的示例，实际应用中可能需要根据具体任务进行修改和调整。此外，代码中的示例输入特征和邻接矩阵是随机生成的，并不具有实际意义，您需要根据实际情况进行替换。