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Subject [GitHub] [incubator-mxnet] Neutron3529 opened a new pull request #18423: fix misbehave of KLDivLoss
Date Wed, 27 May 2020 23:46:07 GMT

Neutron3529 opened a new pull request #18423:

   ## Description ##
   In the current version of KLDivLoss, the return value is not the same value calculated
by SoftmaxCrossEntropyLoss, which is not documented. It may due to the incorrect settings
which using mean rather than sum dealing with the return value.
   I provide a fix of this setting, which will keep the return value of `KLDivLoss` and `SoftmaxCrossEntropyLoss`
almost the same when `from_logits=False` and `sparse_label=False` are set to these functions
   Now, the behave of KLDivLoss is exactly the same to what the document say.
   to reproduce the misbehave in the current master branch:
   import mxnet as mx
   assert((c-d).abs().sum()==0 and a.shape[-1]==2)
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant
[JIRA issue]( created (except PRs with
tiny changes)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [x] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing distributed
   - Build tests will be added for build configuration changes (e.g. adding a new build option
with NCCL)
   - [x] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments are documented.

   - For new examples, is added to explain the what the example does, the source
of the dataset, expected performance on test set and reference to the original paper if applicable
   - Check the API doc at$PR_ID/$BUILD_ID/index.html
   - [x] To the best of my knowledge, examples are either not affected by this change, or
have been fixed to be compatible with this change
   ### Changes ###
   - [x] Now, the behave of KLDivLoss is exactly the same to what the document say.
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be made.
   - Interesting edge cases to note here

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