# Train model
t_total = time.time()
loss_values = []
bad_counter = 0
best = np.inf
best_epoch = 0
for epoch in range(args.epochs):
loss_values.append(train(epoch))
if loss_values[-1] < best:
if args.cuda:
model.cpu()
torch.save(
model.state_dict(), '{}_{}_{}.pkl'.format(model._get_name(),
args.dataset, epoch))
if args.cuda:
model.cuda()
best = loss_values[-1]
best_epoch = epoch
bad_counter = 0
else:
bad_counter += 1
if bad_counter == args.patience:
print("Patience {0} exceeded. Best in last {0} epochs is {1:.4f}.".
format(args.patience, best))
break
files = glob.glob('{}_{}_*.pkl'.format(model._get_name(), args.dataset))
idx_test = idx_test.cuda()
features, adj, labels = Variable(features), Variable(adj), Variable(labels)
def compute_test():
model.eval()
output = model(features, adj)
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.data.item()),
"accuracy= {:.4f}".format(acc_test.data.item()))
# Load model
filename = glob.glob('{}_{}_*.pkl'.format(model._get_name(), args.dataset))[0]
print("Loading {}...".format(filename))
if args.cuda:
model_location = 'cuda'
else:
model_location = 'cpu'
state_dict = torch.load(filename, map_location=model_location)
model.load_state_dict(state_dict)
model.eval()
print('Loaded {}.'.format(filename))
# Testing
compute_test()
state_dict['gc1.weight']
