請給我 GAT 的實作範例
时间: 2024-05-11 21:19:39 浏览: 9
以下是一个简单的GAT模型实现的示例代码,用于节点分类任务:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
class GATLayer(MessagePassing):
def __init__(self, in_channels, out_channels):
super(GATLayer, self).__init__(aggr='add')
self.fc = nn.Linear(in_channels, out_channels)
self.attn_fc = nn.Linear(2*out_channels, 1)
def forward(self, x, edge_index):
# x: [num_nodes, in_channels]
# edge_index: [2, num_edges]
x = self.fc(x)
# x: [num_nodes, out_channels]
self.flow = 'source_to_target'
return self.propagate(edge_index, x=x)
def message(self, x_i, x_j, edge_index):
# x_i: [num_edges, in_channels]
# x_j: [num_edges, in_channels]
# edge_index: [2, num_edges]
z = torch.cat([x_i, x_j-x_i], dim=1)
# z: [num_edges, 2*out_channels]
attn = torch.sigmoid(self.attn_fc(z))
# attn: [num_edges, 1]
return attn * x_j
class GAT(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
super(GAT, self).__init__()
self.layers = nn.ModuleList()
self.layers.append(GATLayer(in_channels, hidden_channels))
for i in range(num_layers-2):
self.layers.append(GATLayer(hidden_channels, hidden_channels))
self.layers.append(GATLayer(hidden_channels, out_channels))
def forward(self, x, edge_index):
# x: [num_nodes, in_channels]
# edge_index: [2, num_edges]
for layer in self.layers:
x = layer(x, edge_index)
x = F.elu(x)
# x: [num_nodes, out_channels]
return x
```
该模型实现了一个简单的GAT网络,包括多层GATLayer和ELU激活函数。其中GATLayer继承自PyTorch Geometric的MessagePassing类,用于在图上传递消息并计算节点表示。在每个GATLayer中,每个节点的表示通过聚合其邻居的表示和自身的表示来计算,其中邻居的表示与自身的表示之间的差异被用于计算注意力系数,以便更好地捕捉节点之间的关系。最后,将最后一层的节点表示用于分类任务。
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