9/28/2023 0 Comments Pytorch sequential![]() Of the \((i+1)\)-th module in the sequential. The output of the \(i\)-th module should match the input Graph ( DGLGraph or list of DGLGraphs) â The graph(s) to apply modules on. rand ( 32, 4 ) > net (, n_feat ) tensor(,, , ]) forward ( graph, * feats ) ¶ erdos_renyi_graph ( 8, 0.8 )) > net = Sequential ( ExampleLayer (), ExampleLayer (), ExampleLayer ()) > n_feat = torch. _init_ () > def forward ( self, graph, n_feat ): > with graph. Module ): > def _init_ ( self ): > super (). args Sub-modules of torch.nn.Module that will be added to the container in. In the second case, the number of graphs equals the number of modules inside this container. It maintains an ordered list of constituent. DGL supports two modes: sequentially apply GNN modules on 1) the same graph or 2) a list of given graphs. In short, nn.Sequential defines a special kind of Module, the class that presents a module in PyTorch. > import torch > import dgl > import torch.nn as nn > import dgl.function as fn > import networkx as nx > from dgl.nn.pytorch import Sequential > class ExampleLayer ( nn. Class Documentation Inheritance Relationships Base Type public torch::nn::ModuleHolder< SequentialImpl > ( Template Class ModuleHolder) Class Documentation class Sequential : public torch::nn::ModuleHolder A ModuleHolder subclass for SequentialImpl. A sequential container for stacking graph neural network modules.Mode 2: sequentially apply GNN modules on different graphs add_edges (, ) > net = Sequential ( ExampleLayer (), ExampleLayer (), ExampleLayer ()) > n_feat = torch. edata > return n_feat, e_feat > g = dgl. _init_ () > def forward ( self, graph, n_feat, e_feat ): > with graph. ![]() > import torch > import dgl > import torch.nn as nn > import dgl.function as fn > from dgl.nn.pytorch import Sequential > class ExampleLayer ( nn.
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