TAGConv
- class dgl.nn.pytorch.conv.TAGConv(in_feats, out_feats, k=2, bias=True, activation=None)[source]
Bases:
Module来自拓扑自适应图卷积网络的拓扑自适应图卷积层
\[H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k},\]其中 \(A\) 表示邻接矩阵， \(D_{ii} = \sum_{j=0} A_{ij}\) 是其对角度矩阵， \({\Theta}_{k}\) 表示将不同跳数的结果相加的线性权重。
- Parameters:
in_feats (int) – 输入特征大小。即，\(X\)的维度数。
out_feats (int) – 输出特征大小。即，\(H^{K}\) 的维度数。
k (int, optional) – 跳数 \(K\)。默认值：
2。bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True.activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features. Default:
None.
- lin
可学习的线性模块。
- Type:
torch.Module
示例
>>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import TAGConv >>> >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> feat = th.ones(6, 10) >>> conv = TAGConv(10, 2, k=2) >>> res = conv(g, feat) >>> res tensor([[ 0.5490, -1.6373], [ 0.5490, -1.6373], [ 0.5490, -1.6373], [ 0.5513, -1.8208], [ 0.5215, -1.6044], [ 0.3304, -1.9927]], grad_fn=<AddmmBackward>)
- forward(graph, feat, edge_weight=None)[source]
Description
计算拓扑自适应图卷积。
- param graph:
图表。
- type graph:
DGLGraph
- param feat:
The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
- type feat:
torch.Tensor
- param edge_weight:
edge_weight to use in the message passing process. This is equivalent to using weighted adjacency matrix in the equation above, and \(\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}\) is based on
dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm.- type edge_weight:
torch.Tensor, 可选的
- returns:
The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
- rtype:
torch.Tensor