Graph Attention Networks(GAT)代码示例
时间: 2023-07-02 15:10:02 浏览: 191
以下是使用PyTorch实现GAT的代码示例:
``` python
import torch
import torch.nn as nn
import torch.nn.functional as F
class GATLayer(nn.Module):
def __init__(self, in_dim, out_dim):
super(GATLayer, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.W = nn.Parameter(torch.zeros(size=(in_dim, out_dim)))
self.a = nn.Parameter(torch.zeros(size=(2*out_dim, 1)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
nn.init.xavier_uniform_(self.a.data, gain=1.414)
def forward(self, h, adj):
Wh = torch.mm(h, self.W)
a_input = self._prepare_attentional_mechanism_input(Wh)
e = F.leaky_relu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9e15*torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
h_prime = torch.matmul(attention, Wh)
return h_prime
def _prepare_attentional_mechanism_input(self, Wh):
N = Wh.size()[0]
Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
Wh_repeated_alternating = Wh.repeat(N, 1)
all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating], dim=1)
return all_combinations_matrix.view(N, N, 2*self.out_dim)
class GAT(nn.Module):
def __init__(self, n_feat, n_hid, n_class, dropout, alpha, n_heads):
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GATLayer(n_feat, n_hid) for _ in range(n_heads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GATLayer(n_hid*n_heads, n_class)
self.alpha = alpha
def forward(self, x, adj):
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, adj))
return F.log_softmax(x, dim=1)
```
在此示例中,我们实现了一个包含多头注意力机制的GAT模型。其中,GATLayer是GAT的核心组件,每个GATLayer都包含一个注意力头。在GAT模型中,我们将多个注意力头的输出连接在一起,再通过一个输出层进行分类。在forward函数中,我们首先对输入进行dropout,然后通过多个GATLayer进行特征提取,最后通过输出层进行分类并使用log_softmax进行预测。
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