The negative log likelihood loss.

nnf_nll_loss(
  input,
  target,
  weight = NULL,
  ignore_index = -100,
  reduction = "mean"
)

Arguments

input

\((N, C)\) where C = number of classes or \((N, C, H, W)\) in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in the case of K-dimensional loss.

target

\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) 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

ignore_index

(int, optional) Specifies a target value that is ignored and does not contribute to the input gradient.

reduction

(string, 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. Default: 'mean'