The Keras project isn't limited to the core Keras API for building and training neural networks. It spans a wide range of related initiatives that cover every step of the machine learning workflow.
Keras Tuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. Keras Tuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.
AutoKeras is an AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University.
The goal of AutoKeras is to make machine learning accessible for everyone. It provides high-level end-to-end APIs
TextClassifier to solve machine learning problems in a few lines,
as well as flexible building blocks to perform architecture search.
Managed by the Keras team at Google, TensorFlow Cloud is a set of utilities to help you run large-scale
Keras training jobs on GCP with very little configuration effort. Running your experiments on 8 or more GPUs in the cloud
should be as easy as calling
TensorFlow Lite is a runtime for efficient on-device inference that has native support for Keras models. Deploy your models on Android, iOS, or on embedded devices.
The TensorFlow Model Optimization Toolkit is a set of utilities to make your inference models faster, more memory-efficient, and more power-efficient, by performing post-training weight quantization and pruning-aware training. It has native support for Keras models, and its pruning API is built directly on top on the Keras API.
TFX is an end-to-end platform for deploying and maintaining production machine learning pipelines. TFX has native support for Keras models.