hidden_dim: int = 16, dropout_rate: float = 0.5, ) -> None: super().__init__() self.dropout1 = torch.nn.Dropout(dropout_rate) self.conv1 = GCNConv(num_node_features, hidden_dim) self.relu = torch.nn.ReLU(inplace=True) self.dropout2 = torch.nn.Dropout(dropout_rate) self.con...
nn.Linear(dataset.num_node_features, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, dataset.num_classes) ) def forward(self, data): x = data.x # only using node features (x) output = self.layers(x) return output 我们用一个普通的Pytorch训练/验证流程来定义训练和...
num_features, hidden_channels) self.conv2 = GCNConv(hidden_channels, dataset.num_classes) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() x = F.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x model = GCN(...
print(dataset.num_classes) print(dataset[0].num_nodes) print(dataset[0].num_edges) print(dataset[0].num_features) 1. 2. 3. 4. 5. 可以看到这个数据集包含三个分类任务,共19,717个结点,88,648条边,节点特征维度为500。 参考资料 InMemoryDataset官方文档:torch_geometric.data.InMemoryDataset Data...
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self.num_shuffle = args.num_shuffle self.save_dir = args.save_dir self.enhance = args.enhance self.args = args self.is_weighted = self.data.edge_attrisnotNone 开发者ID:THUDM,项目名称:cogdl,代码行数:24,代码来源:unsupervised_node_classification.py ...
RandomNodeSplit(num_val=500, num_test=500), ]) def main(): dataset = AttributedGraphDataset(root="/data/ogb/", name="mag",transform=transform) data = dataset[0] device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") torch.manual_seed(12345) model = SAGE(data....
dl_test = DataLoader(ds_test,batch_size = BATCH_SIZE,num_workers=4) for features,labels in dl_train: print(features) print(labels) break 1. 2. 3. 4. 5. 6. 7. 最后构建模型测试一下数据集管道是否可用。 import torch from torch import nn ...
fa=dict(node_indices=np.arange(39,0,-2)))# add more features (with shared node indices)ds3 = hstack((ds, ds, ds)) radius =2.5inner_radius =1.0# Makes sure it raises error if inner_radius is >= radiusassert_raises(ValueError,lambda: queryengine.SurfaceRingQueryEngine(surface=s2, ...
3. args.task = 'node_classification' 4. args.dataset = 'cora' 5. args.model = 'gcn' 6. # 建立数据集 7. dataset = build_dataset(args) 8. args.num_features = dataset.num_features 9. args.num_classes = dataset.num_classes