François Chollet+ Your Authors @fchollet Deep learning @google. Creator of Keras, neural networks library. Author of 'Deep Learning with Python'. Opinions are my own. Nov. 25, 2019 1 min read + Your Authors

Built-in losses and metrics in Keras follow the signature `loss(y_true, y_pred, sample_weight=None)`. If you have exotic losses or metrics, a simple way to add them w/o having to implement your own training loop from scratch is to define them in an "endpoint layer". Like this:

It feels a bit more like the "Estimator" training style.

Full Colab notebook:  https://colab.research.google.com/drive/1zzLcJ2A2qofIvv94YJ3axRknlA6cBSIw …

This usage pattern enables you to use `fit`/etc with losses or metrics that have completely arbitrary signatures. Such endpoint layer may also have different behavior during training or inference.


You can follow @fchollet.



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