| Submodule | Maintainers | Contact Info |
|---|---|---|
| contrastive | @WindQAQ | windqaq@gmail.com |
| focal_loss | @SSaishruthi | saishruthi.tn@gmail.com |
| lifted | ||
| npairs | @WindQAQ | windqaq@gmail.com |
| sparsemax_loss | @AndreasMadsen | amwwebdk+github@gmail.com |
| triplet |
| Submodule | Loss | Reference |
|---|---|---|
| contrastive | ContrastiveLoss | http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf |
| focal_loss | SigmoidFocalCrossEntropy | https://arxiv.org/abs/1708.02002 |
| lifted | LiftedStructLoss | https://arxiv.org/abs/1511.06452 |
| npairs | NpairsLoss | http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf |
| npairs | NpairsMultilabelLoss | http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf |
| sparsemax_loss | SparsemaxLoss | https://arxiv.org/abs/1602.02068 |
| triplet | TripletSemiHardLoss | https://arxiv.org/abs/1503.03832 |
In order to conform with the current API standard, all losses must:
- Inherit from
keras.losses.Loss. - Register as a keras global object so it can be serialized properly.
- Add the addon to the
py_libraryin this sub-package's BUILD file.
- Simple unittests that demonstrate the loss is behaving as expected on some set of known inputs and outputs.
- When applicable, run all tests with TensorFlow's
@run_in_graph_and_eager_modes(for test method) orrun_all_in_graph_and_eager_modes(for TestCase subclass) decorator. - Add a
py_testto this sub-package's BUILD file.
- Update the table of contents in the project's central README.
- Update the table of contents in this sub-package's README.