GlobalAttentionPooling
- class dgl.nn.pytorch.glob.GlobalAttentionPooling(gate_nn, feat_nn=None)[源代码]
基类：
Module来自 Gated Graph Sequence Neural Networks 的全局注意力池化。
\[r^{(i)} = \sum_{k=1}^{N_i}\mathrm{softmax}\left(f_{gate} \left(x^{(i)}_k\right)\right) f_{feat}\left(x^{(i)}_k\right)\]- 参数:
gate_nn (torch.nn.Module) – 用于计算每个特征的注意力分数的神经网络。
feat_nn (torch.nn.Module, 可选) – 在将每个特征与注意力分数结合之前应用于它们的神经网络。
示例
以下示例使用 PyTorch 后端。
>>> import dgl >>> import torch as th >>> from dgl.nn import GlobalAttentionPooling >>> >>> g1 = dgl.rand_graph(3, 4) # g1 is a random graph with 3 nodes and 4 edges >>> g1_node_feats = th.rand(3, 5) # feature size is 5 >>> g1_node_feats tensor([[0.8948, 0.0699, 0.9137, 0.7567, 0.3637], [0.8137, 0.8938, 0.8377, 0.4249, 0.6118], [0.5197, 0.9030, 0.6825, 0.5725, 0.4755]]) >>> >>> g2 = dgl.rand_graph(4, 6) # g2 is a random graph with 4 nodes and 6 edges >>> g2_node_feats = th.rand(4, 5) # feature size is 5 >>> g2_node_feats tensor([[0.2053, 0.2426, 0.4111, 0.9028, 0.5658], [0.5278, 0.6365, 0.9990, 0.2351, 0.8945], [0.3134, 0.0580, 0.4349, 0.7949, 0.3891], [0.0142, 0.2709, 0.3330, 0.8521, 0.6925]]) >>> >>> gate_nn = th.nn.Linear(5, 1) # the gate layer that maps node feature to scalar >>> gap = GlobalAttentionPooling(gate_nn) # create a Global Attention Pooling layer
情况 1：输入单个图
>>> gap(g1, g1_node_feats) tensor([[0.7410, 0.6032, 0.8111, 0.5942, 0.4762]], grad_fn=<SegmentReduceBackward>)
情况 2：输入一批图
构建一批 DGL 图并将所有图的节点特征连接到一个张量中。
>>> batch_g = dgl.batch([g1, g2]) >>> batch_f = th.cat([g1_node_feats, g2_node_feats], 0) >>> >>> gap(batch_g, batch_f) tensor([[0.7410, 0.6032, 0.8111, 0.5942, 0.4762], [0.2417, 0.2743, 0.5054, 0.7356, 0.6146]], grad_fn=<SegmentReduceBackward>)
注意
请参阅我们的 GGNN 示例，了解如何使用 GatedGraphConv 和 GlobalAttentionPooling 层构建可以解决数独的图神经网络。