加入注意力机制的GCN代码
时间: 2023-06-11 09:07:40 浏览: 76
以下是加入注意力机制的GCN代码示例:
```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.W = nn.Parameter(torch.randn(in_features, out_features))
self.b = nn.Parameter(torch.randn(out_features))
def forward(self, x, adj):
x = torch.mm(x, self.W)
x = torch.spmm(adj, x)
x = x + self.b
return F.relu(x)
class GraphAttention(nn.Module):
def __init__(self, in_features, out_features):
super(GraphAttention, self).__init__()
self.W = nn.Parameter(torch.randn(in_features, out_features))
self.a = nn.Parameter(torch.randn(out_features, 1))
self.b = nn.Parameter(torch.randn(out_features))
def forward(self, x, adj):
h = torch.mm(x, self.W)
e = torch.matmul(torch.tanh(torch.mm(h, self.a)), self.b)
attention = F.softmax(e, dim=0)
x = torch.spmm(adj, h * attention)
return F.relu(x)
class GCN(nn.Module):
def __init__(self, in_features, hidden_features, out_features):
super(GCN, self).__init__()
self.gc1 = GraphAttention(in_features, hidden_features)
self.gc2 = GraphAttention(hidden_features, out_features)
def forward(self, x, adj):
x = self.gc1(x, adj)
x = self.gc2(x, adj)
return x
```
其中,GraphAttention是加入了注意力机制的GCN层,包含了一个权值矩阵self.W和注意力矩阵self.a。在forward函数中,先对输入特征进行线性变换,然后计算注意力矩阵e,最后通过softmax函数得到注意力系数attention,将注意力系数乘上特征矩阵h,得到加权特征矩阵x。最后再通过稀疏矩阵乘法将加权特征矩阵与邻接矩阵相乘,得到输出特征矩阵。
GCN类中,gc1和gc2是两个GraphAttention层,分别连接输入层和隐藏层,以及隐藏层和输出层。在forward函数中,通过两个GraphAttention层进行特征提取和转换,最后得到输出特征矩阵。