This is a completely new implementation of the TensorFlow Lattice library. It is *not* backwards compatible with the previous versions of the library.
Changes:
* **Core TF implementation**: TFL v2 is a python-only library with all operations implemented in core TensorFlow, making it compatible with any platform that can run TensorFlow (cpu, gpu, tpu).
* **Keras layers**: The new library provides Keras layers that can be mixed and matched with other Keras layers and used in Keras models. All constraints and regularizers are handled through Keras mechanisms and should work seamlessly without the need for hooks or callbacks.
* **New and improved canned estimators**: The new library has a new simplified API for creating canned estimators. Calculation of feature quantiles is now automated in estimator construction. A version of the Crystals algorithm is now supported.
* **New constraint types**: Several new types of constraints are added to the library, including convexity, unimodality, and pairwise feature trust and dominance relations.
* **Improved documentation and tutorials**: Examples and tutorials are provided as notebooks. All documentations, examples, and API docs will be available on tensorflow.org.
* **Faster release cycle**: With the library implemented in core TF, we hope to be able to release updates and improvements more frequently.
Notes:
* Some of the new 2-dimensional constraints are under active development and might undergo API changes.
PyPI release:
* Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.