classNodeApplyModule(nn.Module):"""将节点特征 hv 更新为 ReLU(Whv+b)."""def__init__(self, in_feats, out_feats, activation): super(NodeApplyModule, self).__init__() self.linear = nn.Linear(in_feats, out_feats) self.activation = activationdefforward(self, node): h = self.linear(...
AI代码解释 classGCN(nn.Module):def__init__(self,in_feats,out_feats,activation):super(GCN,self).__init__()self.apply_mod=NodeApplyModule(in_feats,out_feats,activation)defforward(self,g,feature):g.ndata['h']=feature g.update_all(gcn_msg,gcn_reduce)g.apply_nodes(func=self.apply_mod)...
self.apply_mod = NodeApplyModule(in_feats, out_feats, activation) def forward(self, g, feature): # 使用 h 初始化节点特征。 g.ndata['h'] = feature # 使用 update_all接口和自定义的消息传递及累和函数更新节点表示。 g.update_all(msg, reduce) g.apply_nodes(func=self.apply_mod) return ...
DGLGraph.update_all(message_func, reduce_func, apply_node_func=None, etype=None) 可以看出,它接受四个参数,我们主要解释前三个: message_func是消息函数,是边接受源节点的信息.进行节点间消息传递自然是通过边进行,这个函数就是完成源节点特征与边特征的处理. reduce_func是聚合函数,按字面意思应理解为"归...
classNodeApplyModule(nn.Module):"""将节点特征 hv 更新为 ReLU(Whv+b)."""def__init__(self,in_feats,out_feats,activation):super(NodeApplyModule,self).__init__()self.linear=nn.Linear(in_feats,out_feats)self.activation=activationdefforward(self,node):h=self.linear(node.data['h'])h=self...
self.apply_mod = NodeApplyModule(in_feats, out_feats, activation) def forward(self, g, feature): g.ndata['h'] = feature g.update_all(gcn_msg, gcn_reduce) g.apply_nodes(func=self.apply_mod) return g.ndata.pop('h') 1.
ndata=core.message_passing(g,message_func,reduce_func,apply_node_func) message_passing会完成message,aggregate和update这三个过程,首先会判断传入的mfunc和rfunc是不是DGL预定义的函数,DGL会对预定义的函数做优化,DGL为mfunc和rfunc都是预定义的算子的情况都写了kernel ...
DGLGraph.multi_update_all(etype_dict, cross_reducer, apply_node_func=None): etype_dict: dict类型, 键为一种关系, 值为这种关系对应的update_all()的参数; cross_reducer: str类型, 表示跨类型整...
input nodes 为 采样的subgraph中的所有的节点的集合for input_nodes, pos_g, neg_g, blocks in tqdm.tqdm(spec_dataloader): emb = extract_embed(all_node_embed, input_nodes) pos_score, neg_score = model(emb, pos_g, neg_g, blocks, etype) loss = loss_func(pos_score, neg_score...
在DGL中每条关系使用三元组来表示(source node type, edge type, destination node type) >>>importdgl>>>importtorchasth>>># Create a heterograph with 3 node types and 3 edges types.>>>graph_data = {...('drug','interacts','drug'): (th.tensor([0,1]), th.tensor([1,2])),...('...