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'none' | 'mean' | 'sum' Specifies a target value that is ignored and does not contribute to the input gradients. No reduction will be applied, 'mean'

The sum of the output will be divided by the number of elements in the output, 'sum' Average factor that is used to average the loss The output will be summed.

本文深入探讨PyTorch中的F.cross_entropy ()函数，解释其内部运作机制，特别是如何处理目标变量target，以及为何target可以是标量而非one-hot编码。

This name will be used to combine different loss items by simple sum operation In addition, if you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name The name of this loss item 本文深入解析Pytorch中交叉熵损失函数cross_entropy的使用方法，包括参数详解及实例演示，帮助理解softmaxLoss的实现与调整。

Sum depends on the number of data points, obviously It is still valid and often used (e.g When comparable scales in a custom compound loss are needed), assuming the most popular implementations of minibatch learning. While experimenting with my model i see that the various loss classes for pytorch will accept a reduction parameter (none | sum | mean) for example

The differences are rather obvious regarding what will be returned, but i’m curious when it would be useful to use sum as opposed to mean?

Options are 'none', 'mean' and 'sum'

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