| benchmarks | ||
| examples | ||
| integration_tests | ||
| keras_core | ||
| shell | ||
| .gitignore | ||
| demo_functional.py | ||
| jax_integration_test.py | ||
| jax_training_scratchpad.py | ||
| LICENSE | ||
| pip_build.py | ||
| pyproject.toml | ||
| README.md | ||
| requirements.txt | ||
| setup.cfg | ||
| setup.py | ||
| tf_integration_test.py | ||
Keras Core: a new multi-backend Keras
Keras Core is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
Backwards compatibility
Keras Core is intend to work as a drop-in replacement for tf.keras (when using the TensorFlow backend).
In addition, Keras models can consume datasets in any format, regardless of the backend you're using: you can train your models with your existing tf.data.Dataset pipelines or Torch DataLoaders.
Why use Keras Core?
- Write custom components (e.g. layers, models, metrics) that you can move across framework boundaries.
- Make your code future-proof by avoiding framework lock-in.
- As a PyTorch user: get access to power of Keras, at last!
- As a JAX user: get access to a fully-featured, battle-tested modeling and training library.
Credits
TODO