Laplacian regularizer for PWL calibration layer.

Calibrator Laplacian regularization penalizes the change in the calibration output. It is defined to be:

l1 * ||delta||_1 + l2 * ||delta||_2^2

where delta is:

output_keypoints[1:end] - output_keypoints[0:end-1].

l1 l1 regularization amount as float.
l2 l2 regularization amount as float.
is_cyclic Whether the first and last keypoints should take the same output value.



Creates a regularizer from its config.

This method is the reverse of get_config, capable of instantiating the same regularizer from the config dictionary.

This method is used by TF-Keras model_to_estimator, saving and loading models to HDF5 formats, TF-Keras model cloning, some visualization utilities, and exporting models to and from JSON.

config A Python dictionary, typically the output of get_config.

A regularizer instance.


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Standard Keras config for serialization.


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Returns regularization loss.

x Tensor of shape: (k, units) which represents weights of PWL calibration layer. First row of weights is bias term. All remaining represent delta in y-value compare to previous point (segment heights).