README.md

Addons - Losses

Maintainers

Submodule	Maintainers	Contact Info
contrastive	@WindQAQ	[email protected]
focal_loss	@SSaishruthi	[email protected]
giou_loss	@fsx950223	[email protected]
lifted	@rahulunair	[email protected]
npairs	@WindQAQ	[email protected]
sparsemax_loss	@AndreasMadsen	[email protected]
triplet	@rahulunair	[email protected]

Components

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
giou_loss	GIoULoss	https://giou.stanford.edu/GIoU.pdf
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

Contribution Guidelines

Standard API

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: @tf.keras.utils.register_keras_serializable(package='Addons')
• Add the addon to the py_library in this sub-package's BUILD file.

Testing Requirements

• 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) or run_all_in_graph_and_eager_modes (for TestCase subclass) decorator.
• Add a py_test to this sub-package's BUILD file.

Documentation Requirements

• Update the table of contents in the project's central README.
• Update the table of contents in this sub-package's README.