Function train

src/train_new.py:162–207

(epoch, train_adj, train_fea, idx_train, val_adj=None, val_fea=None)

# define the training function.
def train(epoch, train_adj, train_fea, idx_train, val_adj=None, val_fea=None):
    if val_adj is None:
        val_adj = train_adj
        val_fea = train_fea

    t = time.time()
    model.train()
    optimizer.zero_grad()
    output = model(train_fea, train_adj)
    # special for reddit
    if sampler.learning_type == "inductive":
        loss_train = F.nll_loss(output, labels[idx_train])
        acc_train = accuracy(output, labels[idx_train])
    else:
        loss_train = F.nll_loss(output[idx_train], labels[idx_train])
        acc_train = accuracy(output[idx_train], labels[idx_train])

    loss_train.backward()
    optimizer.step()
    train_t = time.time() - t
    val_t = time.time()
    # We can not apply the fastmode for the reddit dataset.
    # if sampler.learning_type == "inductive" or not args.fastmode:

    if args.early_stopping > 0 and sampler.dataset != "reddit":
        loss_val = F.nll_loss(output[idx_val], labels[idx_val]).item()
        early_stopping(loss_val, model)

    if not args.fastmode:
        #    # Evaluate validation set performance separately,
        #    # deactivates dropout during validation run.
        model.eval()
        output = model(val_fea, val_adj)
        loss_val = F.nll_loss(output[idx_val], labels[idx_val]).item()
        acc_val = accuracy(output[idx_val], labels[idx_val]).item()
        if sampler.dataset == "reddit":
            early_stopping(loss_val, model)
    else:
        loss_val = 0
        acc_val = 0

    if args.lradjust:
        scheduler.step()

    val_t = time.time() - val_t
    return (loss_train.item(), acc_train.item(), loss_val, acc_val, get_lr(optimizer), train_t, val_t)


def test(test_adj, test_fea):