Equinox is a JAX library for building neural networks and other parameterised models. It provides a PyTorch-like approach to defining models while retaining compatibility with JAX and its wider ecosystem.
Models are represented as PyTrees, allowing them to work naturally with JAX transformations such as automatic differentiation, JIT compilation, and vectorisation.
This is free and open source software.
Key Features
- Build neural networks and other parameterised models with JAX.
- PyTorch-like syntax for defining models.
- Represents models as standard JAX PyTrees.
- Works with JAX transformations including JIT compilation and automatic differentiation.
- Provides filtered versions of common JAX transformations.
- Includes utilities for manipulating and combining PyTrees.
- Supports runtime error checking inside compiled code.
- Fully interoperable with other libraries in the JAX ecosystem.
- Lightweight design without imposing a separate framework abstraction.
Website: https://github.com/patrick-kidger/equinox
Support:
Developer: Patrick Kidger
License: Apache License 2.0
Equinox is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Deep Learning with Python | |
|---|---|
| TensorFlow | A very popular Deep Learning framework |
| PyTorch | Tensors and Dynamic neural networks in Python |
| Keras | High-level neural networks API |
| fastai | Simplifies training fast and accurate neural nets using modern best practices |
| PyTensor | Library for fast numerical computation |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for Artificial Neural Networks and Deep Learning |
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