import torch.nn as nn import torch.nn.functional as F from dgl.data import * 构造一个两层的gnn模型 class SAGE(nn.Module): def __init__(self, in_feats, hid_feats, out_feats, dropout=0.2): super().__init__() self.conv1 = dglnn.SAGEConv( in_feats=in_feats, out_feats=hid_fea...
例如,以下代码创建了一个 PyTorch 的 DataLoader,它分批迭代训练节点 ID 数组 train_nids, 并将生成的子图列表放到 GPU 上: import dgl import dgl.nn as dglnn import torch import torch.nn as nn import torch.nn.functional as F sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2) dataloader = ...
在SAGEConv中,子模块根据聚合类型而有所不同。这些模块是纯PyTorch NN模块,例如nn.Linear、nn.LSTM等。 构造函数的最后调用了reset_parameters()进行权重初始化。    1defreset_parameters(self):2"""重新初始化可学习的参数"""3gain = nn.init.calcul...
但是,在 :class:~dgl.nn.pytorch.conv.SAGEConv模块中,被聚合的特征将会与节点的初始特征拼接起来, forward()函数的输出不会全为0。在这种情况下,无需进行此类检验。 DGL NN模块可在不同类型的图输入中重复使用,包括:同构图、异构图(:ref:guide_cn-graph-heterogeneous)和子图块(:ref:guide_cn-minibatch)。
from dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils import makedirs, save_info, load_infofrom sklearn.metrics import roc_auc_scoreimport gcgc...
import torch.nn.functional as Ffrom dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils import makedirs, save_info, load_infofrom sklearn.metrics ...
load_graphsimport dgl.function as fnimport torchimport dglimport torch.nn.functional as Ffrom dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils imp...
import torch.nn as nn import torch.nn.functional as F classSAGE(nn.Module): def __init__(self, in_size, hid_size, out_size): super().__init__() self.layers = nn.ModuleList() # three-layer GraphSAGE-mean self.layers.append(SAGEConv(in_size, hid_size,"mean")) ...
load_graphsimport dgl.function as fnimport torchimport dglimport torch.nn.functional as Ffrom dgl.nn.pytorch import GraphConv, SAGEConv, HeteroGraphConvfrom dgl.utils import expand_as_pairimport tqdmfrom collections import defaultdictimport torch as thimport dgl.nn as dglnnfrom dgl.data.utils imp...
DGL-KE vs Pytorch-Big-Graph on Freebase DGL-KE 的 github 项目主页: https://github.com/awslabs/dgl-ke DGL-LifeSci:面向化学和生物领域的 GNN 算法库 作为之前在 DGL 项目下的 model zoo,DGL-LifeSci 现在也正式成为独立的软件包,其亮点主要如下: 提供不同应用的训练脚本和预训练模型,包括:molecular ...