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