f31488d65a98107e03f2045f3c5e2f927dab067d,03-wordemb-pytorch/wordemb-skip.py,,calc_sent_loss,#,73

Before Change

                context_id = sent[i + direction * j] if 0 <= i + direction * j < len(sent) else S
                context = torch.tensor([context_id]).type(type)   // Tensor for context word
                loss = model(c, context)
                if not inference:
                    // Back prop while training only
                    optimizer.zero_grad()
                    loss.backward()
                    optimizer.step()
                total_loss += loss.data.cpu().item()
    return total_loss

After Change

    // as we need to predict the eos as well, the future window at that point is N past it

    // Step through the sentence
    losses = []
    for i, word in enumerate(sent):
        for j in range(1, N + 1):
            for direction in [-1, 1]:
                c = torch.tensor([word]).type(type)  // This is tensor for center word
                context_id = sent[i + direction * j] if 0 <= i + direction * j < len(sent) else S
                context = torch.tensor([context_id]).type(type)   // Tensor for context word
                logits = model(c)
                loss = criterion(logits, context)
                losses.append(loss)
    return torch.stack(losses).sum()


MAX_LEN = 100

Instances