def train()

in word_language_model/main.py [0:0]

• 33 lines of code
• 4 McCabe index (conditional complexity)

def train():
    # Turn on training mode which enables dropout.
    model.train()
    total_loss = 0.
    start_time = time.time()
    ntokens = len(corpus.dictionary)
    if args.model != 'Transformer':
        hidden = model.init_hidden(args.batch_size)
    for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
        data, targets = get_batch(train_data, i)
        # Starting each batch, we detach the hidden state from how it was previously produced.
        # If we didn't, the model would try backpropagating all the way to start of the dataset.
        model.zero_grad()
        if args.model == 'Transformer':
            output = model(data)
            output = output.view(-1, ntokens)
        else:
            hidden = repackage_hidden(hidden)
            output, hidden = model(data, hidden)
        loss = criterion(output, targets)
        loss.backward()

        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
        torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
        for p in model.parameters():
            p.data.add_(p.grad, alpha=-lr)

        total_loss += loss.item()

        if batch % args.log_interval == 0 and batch > 0:
            cur_loss = total_loss / args.log_interval
            elapsed = time.time() - start_time
            print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
                    'loss {:5.2f} | ppl {:8.2f}'.format(
                epoch, batch, len(train_data) // args.bptt, lr,
                elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
            total_loss = 0
            start_time = time.time()
        if args.dry_run:
            break