如何处理Pytorch中的小批量损失？

Question

我将迷你批量数据提供给模型，我只想知道如何处理损失。我可以累积损失，然后调用后退，如：

    ...
    def neg_log_likelihood(self, sentences, tags, length):
        self.batch_size = sentences.size(0)

        logits = self.__get_lstm_features(sentences, length)
        real_path_score = torch.zeros(1)
        total_score = torch.zeros(1)
        if USE_GPU:
            real_path_score = real_path_score.cuda()
            total_score = total_score.cuda()

        for logit, tag, leng in zip(logits, tags, length):
            logit = logit[:leng]
            tag = tag[:leng]
            real_path_score += self.real_path_score(logit, tag)
            total_score += self.total_score(logit, tag)
        return total_score - real_path_score
    ...
loss = model.neg_log_likelihood(sentences, tags, length)
loss.backward()
optimizer.step()

我想知道如果积累可能导致梯度爆炸？

那么，我应该调用后向循环：

for sentence, tag , leng in zip(sentences, tags, length):
    loss = model.neg_log_likelihood(sentence, tag, leng)
    loss.backward()
    optimizer.step()

或者，使用平均损失，就像tensorflow中的reduce_mean一样

loss = reduce_mean(losses)
loss.backward()

Answer 1

loss必须使用迷你批量大小减少mean。如果你看一下像CrossEntropyLoss这样的原生PyTorch损失函数，就会有一个单独的参数reduction，默认行为是在迷你批量大小上做mean。