列举图卷积神经网络回归分析模型
时间: 2023-12-21 10:31:01 浏览: 25
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
def __init__(self, in_features, out_features):
super(GraphConvolution, self).__init__()
self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))
self.bias = nn.Parameter(torch.FloatTensor(out_features))
def forward(self, input, adj):
support = torch.mm(input, self.weight)
output = torch.spmm(adj, support)
return output + self.bias
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = self.gc2(x, adj)
return x
```