tfl.lattice_layer.LaplacianRegularizer

Laplacian regularizer for tfl.layers.Lattice layer.

tfl.lattice_layer.LaplacianRegularizer(
    lattice_sizes, l1=0.0, l2=0.0
)

Laplacian regularizer penalizes the difference between adjacent vertices in multi-cell lattice (see publication).

Consider a 3 x 2 lattice with weights w:

w[3]-----w[4]-----w[5]
  |        |        |
  |        |        |
w[0]-----w[1]-----w[2]

where the number at each node represents the weight index. In this case, the laplacian regularizer is defined as:

l1[0] * (|w[1] - w[0]| + |w[2] - w[1]| +
         |w[4] - w[3]| + |w[5] - w[4]|) +
l1[1] * (|w[3] - w[0]| + |w[4] - w[1]| + |w[5] - w[2]|) +

l2[0] * ((w[1] - w[0])^2 + (w[2] - w[1])^2 +
         (w[4] - w[3])^2 + (w[5] - w[4])^2) +
l2[1] * ((w[3] - w[0])^2 + (w[4] - w[1])^2 + (w[5] - w[2])^2)

lattice_sizes Lattice sizes of tfl.layers.Lattice to regularize.
l1 l1 regularization amount. Either single float or list or tuple of floats to specify different regularization amount per dimension.
l2 l2 regularization amount. Either single float or list or tuple of floats to specify different regularization amount per dimension.

ValueError If provided input does not correspond to lattice_sizes.

Methods

from_config

@classmethod
from_config(
    config
)

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.

Args
config A Python dictionary, typically the output of get_config.

Returns
A regularizer instance.

get_config

View source

get_config()

Standard Keras config for serialization.

__call__

View source

__call__(
    x
)

Returns regularization loss for x.