| import torch # pytorch backend | |
| from grb.dataset import Dataset | |
| from grb.model.torch import GCN | |
| from grb.utils.trainer import Trainer | |
| # Load data | |
| # name: ["grb-cora", "grb-citeseer", "grb-aminer", "grb-reddit", "grb-flickr"]. | |
| # mode: [["easy", "medium", "hard", "full"] | |
| # mode: | |
| dataset = Dataset(name='grb-citeseer', mode='easy',feat_norm='None') | |
| features = dataset.features # 注意：不是 tensor，而是 scipy.sparse | |
| adj = dataset.adj # 是 numpy.ndarray 或 scipy.sparse | |
| labels = dataset.labels | |
| index = dataset.idx_train | |
| print(type(features)) | |
| print(type(adj)) | |
| print(type(labels)) | |
| print(type(index)) | |
| # model = GCN(in_features=dataset.num_features, | |
| # out_features=dataset.num_classes, | |
| # hidden_features=[64, 64]) | |
| # # Training | |
| # adam = torch.optim.Adam(model.parameters(), lr=0.01) | |
| # trainer = Trainer(dataset=dataset, optimizer=adam, | |
| # loss=torch.nn.functional.nll_loss) | |
| # trainer.train(model=model, n_epoch=200, dropout=0.1, | |
| # train_mode='inductive') | |