如何解决pytorch 如何更加重视聚合特征、注意力机制
def forward(self,nodes_batch):
"""
...
#Initiate self feature of center node
pre_hidden_embs = self.raw_features
for index in range(1,self.num_layers+1):
#nb = lower_layer_nodes of 1st order,followed by 2nd order
nb = nodes_batch_layers[index][0]
#Extract 3 tuples from 2nd order,followed by 1st
pre_neighs = nodes_batch_layers[index-1]
# self.dc.logger.info('aggregate_feats.')
#Aggregate 1st layer unique nodes,self,2nd layer unique nodes/direct neighs + self
#Aggregate 2nd layer unique nodes,aggregate_feats = self.aggregate(nb,pre_hidden_embs,pre_neighs)
sage_layer = getattr(self,'sage_layer'+str(index))
if index > 1:
#_node_map returns index of lower_layer_nodes_dict --> unique center nodes of layer 0
nb = self._nodes_map(nb,pre_neighs)
**#aggregate_feats = 2*self.aggregate(nb,pre_neighs)**
# self.dc.logger.info('sage_layer.')
#W/ nb index,retrieve self + aggregate_feats embeddings (2nd order +1st,then 1st + zero layers)
cur_hidden_embs = sage_layer(self_feats=pre_hidden_embs[nb],aggregate_feats=aggregate_feats)
#Agg neigh of 2nd layer to 1st layer
#Then aggreg 1st layer(aggregated earlier) to zero layer
#From outside to inside
pre_hidden_embs = cur_hidden_embs
return pre_hidden_embs
请注意以下已停用的内容,如果它是第一层到中心节点,我计划为这个aggregate_feats分配更多权重,如果它是第二层到第一层,则为aggregate_feats分配更少权重。我可以知道如何实现这一目标吗? #aggregate_feats = 2*self.aggregate(nb,pre_hidden_embs,pre_neighs)
换句话说,我如何为某个嵌入分配更多的权重,在这种情况下,它是aggregate_feats。
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