torch.nn.functional.nll_loss(input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean')[source]

The negative log likelihood loss.

See NLLLoss for details.

  • input (Tensor) – (N,C)(N, C) where C = number of classes or (N,C,H,W)(N, C, H, W) in case of 2D Loss, or (N,C,d1,d2,...,dK)(N, C, d_1, d_2, ..., d_K) where K1K \geq 1 in the case of K-dimensional loss. input is expected to be log-probabilities.

  • target (Tensor) – (N)(N) where each value is 0targets[i]C10 \leq \text{targets}[i] \leq C-1, or (N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K) where K1K \geq 1 for K-dimensional loss.

  • weight (Tensor, optional) – a manual rescaling weight given to each class. If given, has to be a Tensor of size C

  • size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True

  • ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is True, the loss is averaged over non-ignored targets. Default: -100

  • reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True

  • reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

Return type:



>>> # input is of size N x C = 3 x 5
>>> input = torch.randn(3, 5, requires_grad=True)
>>> # each element in target has to have 0 <= value < C
>>> target = torch.tensor([1, 0, 4])
>>> output = F.nll_loss(F.log_softmax(input, dim=1), target)
>>> output.backward()


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