torch_geometric gatconv定义 torch_geometric中的GATConv(Graph Attention Network)模块是用于图形数据的一种卷积神经网络模块。它基于注意力机制,可以对图中节点的特征进行编码和聚合。 GATConv在每个节点处计算出它周围节点的重要性权重,然后使用这些权重聚合邻居节点的特征,最终将所有邻居的信息进行整合,生成一个节点的...
# x = conv(x, edge_index) print(x) Actually, aggr_out is the x. Note 1. In step 4.2, if we change theedge_index edge_index = torch.tensor([[1, 2, 0], [0, 0, 3]], dtype=torch.long) 2. Here, we did not try multi-heads, also, we use average heads rather than concat...
from torch_geometric.nn import GATConv, GraphConv, TopKPooling class CustomeModel(torch.nn.Module): def __init__(self): super(CustomeModel, self).__init__() self.gcn_layer = GraphConv(512, 256) def forward(self, x1, x2): x, edge_index = x1, x2 x = self.gcn_layer(x, edge...
Source File: test_gcn_conv.py From pytorch_geometric with MIT License 5 votes def test_gcn_conv_with_sparse_input_feature(): x = torch.sparse_coo_tensor(indices=torch.tensor([[0, 0], [0, 1]]), values=torch.tensor([1., 1.]), size=torch.Size([4, 16])) edge_index = torch...
PyG用torch_geometric.data.Data保存图结构的数据,导入的data(这个data指的是你导入的具体数据,不是前面那个torch_geometric.data)在PyG中会包含以下属性: data.x:图节点的属性信息,比如社交网络中每个用户是一个节点,这个x可以表示用户的属性信息,维度为[num_nodes, num_node_features] data.edge_index:COO格式...