[GitHub] muralibalki opened a new issue #12185: from_logits definition seems different from what is expected?

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muralibalki opened a new issue #12185: from_logits definition seems different from what is expected?
URL: https://github.com/apache/incubator-mxnet/issues/12185
 
 
   In loss functions like SoftmaxCrossEntropy, the definition of from_logits is:
   from_logits (bool, default False) – Whether input is a log probability (usually from log_softmax) instead of unnormalized numbers.
   
   It seems mxnet calls the output of a log_softmax a logit, whereas others call (any) unscaled log probabilities logits.
   
   e.g. In tensorflow/keras api:
   e.g. https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits_v2
   from_logits: Boolean, whether output is the result of a softmax, or is a tensor of logits.
   
   I think mxnet is consistent with the definition of logits:
   https://en.wikipedia.org/wiki/Logit
   
   But highlighting the difference may be useful in general. I had a bit of trouble converting a model from Keras to Gluon because of this.
   
   

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