tfl.lattice_layer.LinearInitializer

Initializes a tfl.layers.Lattice as linear function.

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

The linear function will have positive coefficients for monotonic dimensions and 0 otherwise. If all dimensions are unconstrained, all coefficients will be positive.
Linear coefficients are set such that the minimum/maximum output of the lattice matches the given output_min/output_max.
Each monotonic dimension contributes with same weight regardless of number of vertices per dimension.
No dimension can be both monotonic and unimodal.
Unimodal dimensions contribute with same weight as monotonic dimensions.
Unimodal dimensions linearly decrease for first (dim_size + 1) // 2 vertices and then linearly increase for following vertices.

Args
`lattice_sizes`	Lattice sizes of `tfl.layers.Lattice` to initialize.
`monotonicities`	Monotonic dimensions for initialization. Does not need to match `monotonicities` of `tfl.layers.Lattice`.
`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 is a mismatch between `monotonicities` and `lattice_sizes`.

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.