View source on GitHub
|
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
get_config
get_config()
Standard Keras config for serialization.
__call__
__call__(
w
)
Applies constraints to w.