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.

tfl.lattice_lib.verify_hyperparameters

Args

Raises