TensorFlow Lattice 2.0.7
Changes:
- (experimental) KroneckerFactoredLattice initialization now sorts on kernel axis 1 such that we sort each term individually.
- (experimental) KroneckerFactoredLattice initialization defaults to [0.5, 1.5] instead of [0,1].
- (experimental) KroneckerFactoredLattice custom_reduce_prod in interpolation for faster gradient computations.
- Update bound and trust projection algorithms to compute violations for each unit separately.
- 'loss_fn' option for estimators to use custom loss without having to define a custom head.
- Enable calibrators to return a list of outputs per unit.
- Enable RTL layer to return non-averaged outputs.
- General tutorial/code cleanup
- Typo fixes
- Bug fixes
PyPI Release:
- Generic package for py3 that should work for TF 1.15 or TF 2.x.