label_smoothed_cross_entropy.py 文件源码

def forward(self, model, sample):
        """Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss, as a Variable
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        net_output = model(**sample['net_input'])
        input = F.log_softmax(net_output.view(-1, net_output.size(-1)))
        target = sample['target'].view(-1)
        loss = LabelSmoothedNLLLoss.apply(input, target, self.eps, self.padding_idx, self.weights)
        sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
        logging_output = {
            'loss': loss.data[0],
            'sample_size': sample_size,
        }
        return loss, sample_size, logging_output