juliusshufan opened a new pull request #9793: Enable the reporting of cross-entropy or nll loss value when training CNN network using the models defined by example/image-classification URL: https://github.com/apache/incubator-mxnet/pull/9793 ## Description ## MxNET already implements the loss computation at Python layer (in python/mxnet/metric.py) For most of the CNN models used for image-classification, as softmax is commonly used as the output, the cross-entropy or likelihood loss value is helpful to monitor the convergence trend during training. The current implementation of example/image-classification/fit.py already provides an extensible implementation to report some useful information during training, such as accuracy. My submission utilizes this design and enabling the report of cross-entropy or nll loss value during training. ## Checklist ## ### Essentials ### - [ ] Passed code style checking (`make lint`) - [ ] 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 - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### All the code changes are in example/image-classification/common/fit.py A new argument '--loss' is introduced, and it can be set as 'ce' or 'nll-loss' corresponding to cross-entropy loss and negative likelihood loss respectively. Taking the train_cifar10.py as an example, the loss value will be reported if the below **bold code line** added, parser.set_defaults( ...... **loss = 'ce'** ...... ) The output log will be the following pattern: INFO:root:Epoch[0] Batch [380] Speed: 504.42 samples/sec accuracy=0.087109 top_k_accuracy_5=0.469531 **cross-entropy=2.305971** INFO:root:Epoch[0] Train-accuracy=0.098437 INFO:root:Epoch[0] Train-top_k_accuracy_5=0.506250 INFO:root:Epoch[0] **Train-cross-entropy=2.304529** INFO:root:Epoch[0] Time cost=100.989 INFO:root:Epoch[0] Validation-accuracy=0.098101 INFO:root:Epoch[0] Validation-top_k_accuracy_5=0.486946 INFO:root:Epoch[0] **Validation-cross-entropy=2.302873** ## 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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