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,
    joint_unimodalities=None,
    output_min=None,
    output_max=None,
    regularization_amount=None,
    regularization_info='',
    interpolation='hypercube'
)

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.
joint_unimodalities	Joint unimodalities 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.
interpolation	One of 'simplex' or 'hypercube' interpolation.

Raises
ValueError	If something is inconsistent.