francois-rozet/inox
Stainless neural networks in JAX
Building and training neural networks can be complex, especially when needing to handle various data types. This tool helps machine learning engineers define neural network architectures with an intuitive, PyTorch-like syntax, allowing them to easily incorporate both traditional numerical parameters and other data like strings or boolean flags. It takes your network definition and data, and outputs a trained model.
Available on PyPI.
Use this if you are a machine learning engineer working with JAX and want a flexible, streamlined way to define neural networks that seamlessly integrate diverse data types beyond just numerical parameters.
Not ideal if you are not using JAX for your neural network development or prefer a framework with a broader set of pre-built, high-level features for common neural network tasks.
Stars
34
Forks
—
Language
Python
License
MIT
Category
Last pushed
Feb 03, 2026
Commits (30d)
0
Dependencies
2
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