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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/03/12 22:59:59 UTC

[GitHub] [incubator-mxnet] abhinavs95 opened a new pull request #14405: [MXNet-1343][WIP][Fit API]Add CNN integration test for fit() API

abhinavs95 opened a new pull request #14405: [MXNet-1343][WIP][Fit API]Add CNN integration test for fit() API
URL: https://github.com/apache/incubator-mxnet/pull/14405
 
 
   ## Description ##
   Add nightly integration tests for fit() API using CNN models.
   This PR depends on the parent PR for fit() API #14346
   JIRA epic: https://issues.apache.org/jira/projects/MXNET/issues/MXNET-1333
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [x] The PR title starts with [MXNet-1343](https://issues.apache.org/jira/browse/MXNET-1343)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] 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 kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
   - [ ] 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, README.md 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 http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - test_estimator_cnn_cpu: test on cpu using synthetic data on alexnet, resnet18_v1, FCN
   - test_estimator_cnn_gpu: test on gpu using MNIST dataset on resnet18_v1 and verify training accuracy
   
   ## 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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