TensorFlow Lattice 2.0.5

Changes:

• Simplex interpolation support for lattices: O(d log(d)) simplex interpolation compared to O(2^d) hypercube interpolation is 2-10x faster with similar or improved training loss.
• RTL layer performance optimization: 2-3x faster and scales much better with wider and deeper models with tens of thousands of lattices.
• Optimization of 2^D hypercube lattices: 10-15% speedup.
• PWL Calibration Sonnet Module (more to come in follow up releases)
• New aggregation function tutorial
• Linear combination support for canned ensemble models.
• Improvement and bug fixes for save/load functionality
• Bug fixes

PyPI release:

• Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.

TensorFlow Lattice 2.0.4

Changes:

• Save/load support for Keras models (HDF5/H5 format)
• RTL layer: An ensemble of Lattice layers that takes in a collection of monotonic and unconstrained features and randomly arranges them into lattices of a given rank.
• AggregateFunction Premade model and Aggregation layer: Applies monotonic function on set inputs passed in as ragged tensors.
• Crystals Lattice ensemble with Premade model
• Feature updates to Lattice layer
• Updates to tutorials
• Bug fixes

PyPI release:

• Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.

TensorFlow Lattice 2.0.3

Changes:

• Two new tutorials: premade models, shape constraints for ML fairness
• Improvements and additions to premade models
• New range dominance for Lattice and Linear layers
• Added 'peak' mode to unimodality constraint
• Updates to documentation
• Bug fixes

PyPI release:

• Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.

TensorFlow Lattice 2.0.2

Changes:

• Adding premade Keras models in tfl.premade module.
• Adding RandomMonotonicInitializer for lattices.
• Several edits to tutorials and API docs.

PyPI release:

• Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.

Notes:

• The API for the premade Keras Models is experimental.
• Creating premade models currently require a fully specified model configuration. We plan to use the new preprocessing mechanism in Keras to support keypoint initialization in future releases.

TensorFlow Lattice 2.0.1

Changes:

• Several edits to tutorials and API docs.
• Bug fixes.

PyPI release:

• Generic package for py2/py3 that should work for TF 1.15 or TF 2.x.

TensorFlow Lattice 2.0.0 [Note: Major Update]

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.

TensorFlow Lattice 0.9.9 [Note: Last Release]

Changes:

• Updating the code base for TF 2.0 compatibility in tf.compat.v1 mode
• Changing tensorflow branch to 1.14
• Changes to build scripts for bazel 0.25.2
• Bug fixes

PyPI release:

• Includes python 2.7 and python 3 on macos and ubuntu
• No gpu binary package is released with 0.9.9

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Important Note:

This is the last release of the current version of the Tensorflow Lattice library. A new version of the library will be released soon:

• Eager compatible base lattice and calibration library implemented in core TF (no custom ops)
• Includes Keras layer and canned estimators
• Not backwards compatible with the current version, but conversion should be easy

TensorFlow Lattice 0.9.8

• Support for linear weight normalization
• New helper functions in lib
• Extending documentation
• Changing tensorflow branch 1.11
• Bug fixes

TensorFlow Lattice 0.9.7

• New base estimator class
• New tflite ops
• New regularizers
• Refactoring
• Changing tensorflow branch 1.9
• Bug fixes

TensorFlow Lattice 0.9.6

• New Estimators for separately-calibrated random tiny lattices: each lattices has its own calibrators for each input feature.
• Updating TensorFlow submodule to r1.5.
• Bug fixes.