soft_cross_entropy#

composer.loss.soft_cross_entropy(input, target, weight=None, ignore_index=- 100, reduction='mean')[source]#

Drop-in replacement for cross_entropy that handles class indices or one-hot labels.

Note

This function will be obsolete with this update.

Parameters

  • input (Tensor) โ€“ \((N, C)\) 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. input is expected to contain unnormalized scores (often referred to as logits).

  • target (Tensor) โ€“ If containing class indices, shape \((N)\) where each value is \(0 \leq \text{targets}[i] \leq C-1\), or \((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of K-dimensional loss. If containing class probabilities, same shape as the input.

  • weight (Tensor, optional) โ€“ a manual rescaling weight given to each class. If given, has to be a Tensor of size C. Default: None.

  • ignore_index (int, optional) โ€“ 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. Note that ignore_index is only applicable when the target contains class indices. Default: -100

  • reduction (str, optional) โ€“ 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'