def test(adj):
''' test on GCN '''
# adj = normalize_adj_tensor(adj)
gcn = GCN(nfeat=features.shape[1],
nhid=16,
nclass=labels.max().item() + 1,
dropout=0.5,
device=device)
gcn = gcn.to(device)
optimizer = optim.Adam(gcn.parameters(), lr=0.01, weight_decay=5e-4)
gcn.fit(features, adj, labels, idx_train) # train without model picking
# gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
output = gcn.output
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:", "loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
return acc_test.item()
def test_gcn(adj, data, cuda, data_prep, nhid=16):
''' test on GCN '''
device = torch.device("cuda" if cuda else "cpu")
features, labels, idx_train, idx_val, idx_test = data_prep(data, device)
gcn = GCN(nfeat=features.shape[1],
nhid=nhid,
nclass=labels.max().item() + 1,
dropout=0.5,
device=device)
gcn = gcn.to(device)
optimizer = optim.Adam(gcn.parameters(), lr=0.01, weight_decay=5e-4)
gcn.fit(features, adj, labels, idx_train) # train without model picking
# gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking
output = gcn.output
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
return acc_test.item()
