Welcome to Neural Geometry’s documentation!#
Warning
The package is still in its early stages. Updates may cause breaking changes.
Neural Geometry is a Python library designed to explore and manipulate the geometric properties of neural network latent spaces. It provides a set of tools and methods to understand the complex, high-dimensional spaces that neural networks operate in, inspired by recent approaches (e.g. Borde et al., 2023).
Features#
The primary features of Neural Geometry include:
An implementation of the neural latent geometry search framework. This framework provides a unique approach to product manifold inference, which can be beneficial in various fields such as machine learning and data analysis.
A selection of optimization methods to cater to different needs and requirements. These methods can be used to fine-tune the performance of the neural latent geometry search framework.
This package is designed to be compatible with popular scientific computing libraries such as NumPy and PyTorch, making it a versatile tool for researchers and developers working in these environments.
Installation#
To install Neural Geometry, you can use pip:
pip install neural-geometry
You can install optional packages for development or visualization using:
pip install .[dev,vis] # install from pyproject.toml
pip install neural-geometry[dev,vis] # install from pypi
Usage#
After installing, you can import the package and use it by following the example.
Contributing#
Contributions to Neural Geometry are welcome! To contribute:
Fork the repository.
Install the pre-commit hooks using
pre-commit install.Create a new branch for your changes.
Make your changes in your branch.
Submit a pull request.
Before submitting your pull request, please make sure your changes pass all tests.
License#
Please refer to the LICENSE file in the repository for information on the project’s license.
Sub-Modules: