tfl.lattice_lib.verify_hyperparameters

Verifies that all given hyperparameters are consistent.

tfl.lattice_lib.verify_hyperparameters(
    lattice_sizes, units=None, weights_shape=None, input_shape=None,
    monotonicities=None, unimodalities=None, edgeworth_trusts=None,
    trapezoid_trusts=None, monotonic_dominances=None, range_dominances=None,
    joint_monotonicities=None, output_min=None, output_max=None,
    regularization_amount=None, regularization_info=''
)

This function does not inspect weights themselves. Only their shape. Use assert_constraints() to assert actual weights against constraints.

See tfl.layers.Lattice class level comment for detailed description of arguments.

Args:

lattice_sizes: Lattice sizes to check againts.
units: Units hyperparameter of Lattice layer.
weights_shape: Shape of tensor which represents Lattice layer weights.
input_shape: Shape of layer input. Useful only if units is set.
monotonicities: Monotonicities hyperparameter of Lattice layer.
unimodalities: Unimodalities hyperparameter of Lattice layer.
edgeworth_trusts: Edgeworth_trusts hyperparameter of Lattice layer.
trapezoid_trusts: Trapezoid_trusts hyperparameter of Lattice layer.
monotonic_dominances: Monotonic dominances hyperparameter of Lattice layer.
range_dominances: Range dominances hyperparameter of Lattice layer.
joint_monotonicities: Joint monotonicities hyperparameter of Lattice layer.
output_min: Minimum output of Lattice layer.
output_max: Maximum output of Lattice layer.
regularization_amount: Regularization amount for regularizers.
regularization_info: String which describes regularization_amount.

Raises:

ValueError: If something is inconsistent.