g = dgl.add_self_loop(g) print(g) #通过 to_bidirected 函数去重 还可以通过nx去重 g = dgl.to_bidirected(g) print(g) 特征工程 基础的图构建好了,我们重点就要处理特征工程了,特征有文本和数字,我们先处理文本。 #加载结巴分词import jieba #进行分词处理 data['text'] = data['item_list']....
import dgl import torch import torch.nn as nn import torch.nn.functional as F from dgl.nn.pytorch import GATConv from dgl import AddSelfLoop from dgl.data import CoraGraphDataset transform = ( AddSelfLoop() ) data = CoraGraphDataset(transform=transform) g = data[0] device = torch.device...
# does not support isolated vertices yet dgl_g = dgl.add_self_loop(dgl_g) cugraph_g = cugraph_dgl.convert.cugraph_storage_from_heterograph(dgl_g, single_gpu=True) 有关如何创建 cuGraph 存储对象的信息,请参阅CuGraphStorage。 创建基于 cuGraph Ops 的模型 在这一步骤中,唯一要做的修改是cugraph_...
defload_cora_data():data=citegrh.load_cora()features=th.FloatTensor(data.features)labels=th.LongTensor(data.labels)mask=th.ByteTensor(data.train_mask)g=data.graph # add self loop g.remove_edges_from(g.selfloop_edges())g=DGLGraph(g)g.add_edges(g.nodes(),g.nodes())returng,features,l...
def forward(self, node): h = self.linear(node.data['h']) h = self.activation(h) return {'h' : h} 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. step 4,我们定义 GCN 的Embedding更新层,以实现在所有节点上进行消息传递,并利用 NodeApplyModule 对节点信息进行计算更新。
g.update_all(fn.copy_u('feat', 'm'), fn.sum('m', 'feat')) deg_in = g.in_degrees().unsqueeze(1) new_feat = torch.pow(deg_in, -0.5) * g.ndata['feat'] return new_feat layer = GCNLayer() g = dgl.add_self_loop(g1)...
>>> g = dgl.add_self_loop(g) >>> feat = th.ones(6, 10) >>> conv = SAGEConv(10, 2, 'pool') >>> res = conv(g, feat) >>> res tensor([[-1.0888, -2.1099], [-1.0888, -2.1099], [-1.0888, -2.1099], [-1.0888, -2.1099], ...
from dgl import AddSelfLoop from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset class GAT(nn.Module): def __init__(self, in_size, hid_size, out_size, heads): super().__init__() self.gat_layers = nn.ModuleList() # two-layer GAT self.gat_layers.append( ...
'calling `g = dgl.add_self_loop(g)` will resolve ' 'the issue. Setting ``allow_zero_in_degree`` ' 'to be `True` when constructing this module will ' 'suppress the check and let the code run.') # copy_src(已被copy_u取代)为内置消息函数,将源节点的特征h复制并发动到mailbox,表示...
atom_featurizer = CanonicalAtomFeaturizer()bond_featurizer = CanonicalBondFeaturizer()g = mol_to_complete_graph(trainmols[0],add_self_loop=False,node_featurizer=atom_featurizer,#edge_featurizer= bond_featurizer) 定义GCN模型 GCNClassifier将gcn_hidden_feats参数作为列表对象。如果想添加n个GCN...