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tfl.lattice_layer.LatticeConstraints

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Constraints for tfl.layers.Lattice layer.

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

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.

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

Methods

from_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

Standard Keras config for serialization.

__call__

View source

Applies constraints to w.