This criterion combines nn_log_softmax()
and nn_nll_loss()
in one single class.
It is useful when training a classification problem with C
classes.
nn_cross_entropy_loss(weight = NULL, ignore_index = 100, reduction = "mean")
weight  (Tensor, optional): a manual rescaling weight given to each class.
If given, has to be a Tensor of size 

ignore_index  (int, optional): Specifies a target value that is ignored
and does not contribute to the input gradient. When 
reduction  (string, optional): Specifies the reduction to apply to the output:

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
is expected to contain raw, unnormalized scores for 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).
This criterion expects a class index in the range \([0, C1]\) as the
target
for each value of a 1D tensor of size minibatch
; if ignore_index
is specified, this criterion also accepts this class index (this index may not
necessarily be in the class range).
The loss can be described as:
$$
\mbox{loss}(x, class) = \log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
= x[class] + \log\left(\sum_j \exp(x[j])\right)
$$
or in the case of the weight
argument being specified:
$$
\mbox{loss}(x, class) = weight[class] \left(x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
$$
The losses are averaged across observations for each minibatch. 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).
Input: \((N, C)\) where C = number of classes
, or
\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
in the case of K
dimensional loss.
Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C1\), or \((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of Kdimensional loss.
Output: scalar.
If 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 Kdimensional loss.
if (torch_is_installed()) { loss < nn_cross_entropy_loss() input < torch_randn(3, 5, requires_grad=TRUE) target < torch_randint(low = 1, high = 5, size = 3, dtype = torch_long()) output < loss(input, target) output$backward() }