tfl.lattice_layer.LatticeConstraints

Constraints for tfl.layers.Lattice layer.

tfl.lattice_layer.LatticeConstraints(
    lattice_sizes,
    monotonicities=None,
    unimodalities=None,
    edgeworth_trusts=None,
    trapezoid_trusts=None,
    monotonic_dominances=None,
    range_dominances=None,
    joint_monotonicities=None,
    joint_unimodalities=None,
    output_min=None,
    output_max=None,
    num_projection_iterations=1,
    enforce_strict_monotonicity=True
)

Applies all constraints to the lattice weights. See tfl.layers.Lattice for more details.

Args
lattice_sizes	Lattice sizes of Lattice layer to constraint.
monotonicities	Same meaning as corresponding parameter of Lattice.
unimodalities	Same meaning as corresponding parameter of Lattice.
edgeworth_trusts	Same meaning as corresponding parameter of Lattice.
trapezoid_trusts	Same meaning as corresponding parameter of Lattice.
monotonic_dominances	Same meaning as corresponding parameter of Lattice.
range_dominances	Same meaning as corresponding parameter of Lattice.
joint_monotonicities	Same meaning as corresponding parameter of Lattice.
joint_unimodalities	Same meaning as corresponding parameter of Lattice.
output_min	Minimum possible output.
output_max	Maximum possible output.
num_projection_iterations	Same meaning as corresponding parameter of Lattice.
enforce_strict_monotonicity	Whether to use approximate projection to ensure that constratins are strictly satisfied.

Raises
ValueError	If weights to project don't correspond to lattice_sizes.

Methods

from_config

@classmethod
from_config(
    config
)

Instantiates a weight constraint from a configuration dictionary.

Example:

constraint = UnitNorm()
config = constraint.get_config()
constraint = UnitNorm.from_config(config)

Args
config	A Python dictionary, the output of get_config.

Returns
A tf.keras.constraints.Constraint instance.

get_config

View source

get_config()

Standard Keras config for serialization.

__call__

View source

__call__(
    w
)

Applies constraints to w.