return loss_val.data.item()
# 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
adj, ori_labels = Variable(adj), Variable(ori_labels)
features = Variable(features)
labels = Variable(labels)
if args.cuda:
model = model.cuda()
features = features.cuda()
adj = adj.cuda()
labels = labels.cuda()
idx_train = idx_train.cuda()
idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
criterion = criterion.cuda()
ori_labels = ori_labels.cuda()
else:
model = model.cpu()
features = features.cpu()
adj = adj.cpu()
labels = labels.cpu()
idx_train = idx_train.cpu()
idx_val = idx_val.cpu()
idx_test = idx_test.cpu()
criterion = criterion.cpu()
ori_labels = ori_labels.cpu()
writer = SummaryWriter(
comment='ori_0.1_hidden_{}_lr_{}_#batch_{}_SampleSize_{}_testBatchSize_{}'.
format(args.hidden, args.lr, args.batchSize, args.sampleSize,
args.testBatchSize))
train_batches = iterate_return(idx_train, args.sampleSize, labels, adj,