class torch.nn.NLLLoss(weight: Optional[torch.Tensor] = None, size_average=None, ignore_index: int = -100, reduce=None, reduction: str = 'mean')
[source]
The negative log likelihood loss. It is useful to train a classification problem with C
classes.
If provided, the optional argument weight
should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set.
The input
given through a forward call is expected to contain log-probabilities of each class. input
has to be a Tensor of size either $(minibatch, C)$ or $(minibatch, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ for the K
-dimensional case (described later).
Obtaining log-probabilities in a neural network is easily achieved by adding a LogSoftmax
layer in the last layer of your network. You may use CrossEntropyLoss
instead, if you prefer not to add an extra layer.
The target
that this loss expects should be a class index in the range $[0, C-1]$ where C = number of classes
; if ignore_index
is specified, this loss also accepts this class index (this index may not necessarily be in the class range).
The unreduced (i.e. with reduction
set to 'none'
) loss can be described as:
where $x$ is the input, $y$ is the target, $w$ is the weight, and $N$ is the batch size. If reduction
is not 'none'
(default 'mean'
), then
Can also be used for higher dimension inputs, such as 2D images, by providing an input of size $(minibatch, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ , where $K$ is the number of dimensions, and a target of appropriate shape (see below). In the case of images, it computes NLL loss per-pixel.
C
. Otherwise, it is treated as if having all ones.reduction
). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are 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
size_average
is True
, the loss is averaged over non-ignored targets.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
'none'
| 'mean'
| 'sum'
. 'none'
: no reduction will be applied, 'mean'
: the weighted mean of the output is taken, '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'
C = number of classes
, or $(N, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K
-dimensional loss.reduction
is 'none'
, then the same size as the target: $(N)$ , or $(N, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K-dimensional loss.Examples:
>>> m = nn.LogSoftmax(dim=1) >>> loss = nn.NLLLoss() >>> # 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 = loss(m(input), target) >>> output.backward() >>> >>> >>> # 2D loss example (used, for example, with image inputs) >>> N, C = 5, 4 >>> loss = nn.NLLLoss() >>> # input is of size N x C x height x width >>> data = torch.randn(N, 16, 10, 10) >>> conv = nn.Conv2d(16, C, (3, 3)) >>> m = nn.LogSoftmax(dim=1) >>> # each element in target has to have 0 <= value < C >>> target = torch.empty(N, 8, 8, dtype=torch.long).random_(0, C) >>> output = loss(m(conv(data)), target) >>> output.backward()
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.NLLLoss.html