self.pool=global_add_pool elifgraph_pooling=="mean": self.pool=global_mean_pool elifgraph_pooling=="max": self.pool=global_max_pool elifgraph_pooling=="attention": self.pool=GlobalAttention(gate_nn=nn.Sequential( nn.Linear(emb_dim,emb_dim),nn.BatchNorm1d(emb_dim),nn.ReLU(),nn.Linear...
代码: importtorchfromtorchimportnnfromtorch_geometric.nnimportglobal_add_pool,global_mean_pool,global_max_pool,GlobalAttention,Set2Setfromgin_nodeimportGINNodeEmbeddingclassGINGraphRepr(nn.Module):def__init__(self,num_tasks=1,num_layers=5,emb_dim=300,residual=False,drop_ratio=0,JK="last",graph...
x, edge_attr, _ = self.meta_layer(x, edge_index, edge_attr) # 这里就是和上一章节讲解的graph pool的方式,有平均,相加和DiffPool if self.pooling == PoolingStrategy.MEAN: x = global_mean_pool(x, data.batch) elif self.pooling == PoolingStrategy.ADD: x = global_add_pool(x, data.batch...
relu() x = global_add_pool(x, batch) x = self.lin1(x).relu() x = F.dropout(x, p=0.5, training=self.training) x = self.lin2(x) return F.log_softmax(x, dim=-1) 定义训练和测试函数 代码语言:javascript 复制 def train(epoch): model.train() if epoch == 51: for param_...
(hidden_channels,out_channels)defforward(self,data):x,edge_index,batch=data.x,data.edge_index,data.batchx=self.conv1(x,edge_index)x=F.relu(x)x=self.conv2(x,edge_index)x=global_max_pool(x,batch)# Need to pool node features to graph level for graph level tasksout=self.lin(x)...
self.pool = global_add_pool elif pooling_method == "mean": self.pool = global_mean_pool elif pooling_method == "max": self.pool = global_max_pool elif pooling_method == "attention": self.pool = GlobalAttention(gate_nn=Linear(self.hidden_channels, 1)) else: raise ValueError("The po...
graph_embeddings=global_add_pool(output,batch) graph_embeddings.clamp_(max=1e6) output=mlpmodel(graph_embeddings) # log_probs = F.log_softmax(output, dim=1) loss = criterion(output, label) return loss,output,label,graph_embeddings def test(args,loader,model,mlpmodel,writer,reverse_mapping...
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction. Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang and Haibin Jiang. COLING 2020 [pdf] Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks. Peng Cui, Le Hu and Yuanchao ...
embed_dim = 128from torch_geometric.nn import TopKPoolingfrom torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmpimport torch.nn.functional as Fclass Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = SAGEConv(embed_dim, 128...
来源 | Medium 【磐创AI导读】：本系列文章为大家介绍了知识图谱与机器学习,这篇文章是上一篇文章：知识...