"_maxK-" + str(max_k) training_log_dir = os.path.join( "./test_log/", test_name) if not os.path.exists( training_log_dir): os.makedirs(training_log_dir) printParOnFile( test_name=test_name, log_dir=training_log_dir, par_list={ "dataset_name": dataset_name, "n_fold": n_folds, "learning_rate": lr, "drop_prob": drop_prob, "weight_decay": weight_decay, "batch_size": batch_size, "n_hidden": n_units, "test_epoch": test_epoch, "aggregator": aggregator, "output": output, "max_k": max_k }) device = torch.device( 'cuda' if torch.cuda.is_available( ) else 'cpu') criterion = torch.nn.NLLLoss() dataset_cv_splits = getcross_validation_split_4_reservoir(
test_type_folder = os.path.join( "./test_log/", test_type) if not os.path.exists(test_type_folder): os.makedirs(test_type_folder) training_log_dir = os.path.join( test_type_folder, test_name) print(test_name) if not os.path.exists(training_log_dir): os.makedirs(training_log_dir) printParOnFile(test_name=test_name, log_dir=training_log_dir, par_list={ "dataset_name": dataset_name, "learning_rate": lr, "dropout": dropout, "weight_decay": weight_decay, "k": k, "test_epoch": test_epoch, "self_loops": self_loops }) graph, features, labels, n_classes, train_mask, test_mask, valid_mask = DGLDatasetReader( dataset_name, self_loops, device) model = GCNetwork(g=graph, in_feats=features.shape[1], n_classes=n_classes, dropout=dropout, k=k, convLayer=LGConv,