tfl.lattice_layer.RandomMonotonicInitializer

Initializes a tfl.layers.Lattice as uniform random monotonic function.

tfl.lattice_layer.RandomMonotonicInitializer(
    lattice_sizes, output_min, output_max, unimodalities=None
)

The uniform random monotonic function will initilaize the lattice parameters uniformly at random and make it such that the parameters are monotonically increasing for each input.
The random parameters will be sampled from [output_min, output_max]

Args
`lattice_sizes`	Lattice sizes of `tfl.layers.Lattice` to initialize.
`output_min`	Minimum layer output after initialization.
`output_max`	Maximum layer output after initialization.
`unimodalities`	None or unimodal dimensions after initialization. Does not need to match `unimodalities` of `tfl.layers.Lattice`.

Raises
`ValueError`	If there are invalid hyperparameters.

Methods

@classmethod
from_config(
    config
)

Instantiates an initializer from a configuration dictionary.

initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

Args
`config`	A Python dictionary, the output of `get_config()`.

Returns
An `Initializer` instance.

get_config()

Standard Keras config for serialization.

__call__(
    shape, dtype=None, partition_info=None
)

Returns weights of tfl.layers.Lattice layer.

Args
`shape`	Must be: `(prod(lattice_sizes), units)`.
`dtype`	Standard Keras initializer param.
`partition_info`	Standard Keras initializer param. Not used.