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TensorFlow Addons is a repository of contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).

User experience and project maintainability are core concepts in TF-Addons. In order to achieve these we require that our additions conform to established API patterns seen in core TensorFlow.

Based on the nature of this repository, there will be contributions that in time become dated and unused. In order to keep the project maintainable, SIG-Addons will perform periodic reviews and deprecate contributions which will be slated for removal. More information will be available after we submit a formal request for comment.

See tensorflow_addons/examples/ for end-to-end examples of various addons.

To install the latest version, run the following:

pip install tensorflow-addons

Note: You will also need TensorFlow 2.0 or higher.

To use addons:

import tensorflow as tf
import tensorflow_addons as tfa

You can also install from source. This requires the Bazel build system.

git clone https://github.com/tensorflow/addons.git
cd addons

# This script links project with TensorFlow dependency
./configure.sh

bazel build build_pip_pkg
bazel-bin/build_pip_pkg artifacts

pip install artifacts/tensorflow_addons-*.whl

Notice: If you are using Mac OS X, you need install addition software by brew install coreutils (this requires the Homebrew) or port install coreutils (this requires the MacPorts)

TF-Addons is a community led open source project. As such, the project depends on public contributions, bug-fixes, and documentation. Please see contribution guidelines for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

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Apache License 2.0