README

Kolmogorov-Arnold Networks (KANs)

This is the github repo for the paper "KAN: Kolmogorov-Arnold Networks" and "KAN 2.0: Kolmogorov-Arnold Networks Meet Science". You may want to quickstart with hellokan, try more examples in tutorials, or read the documentation here.

Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability. A quick intro of KANs here.

 Installation
Pykan can be installed via PyPI or directly from GitHub. 

Pre-requisites:


Python 3.9.7 or higher
pip


For developers


git clone https://github.com/KindXiaoming/pykan.git
cd pykan
pip install -e .


Installation via github


pip install git+https://github.com/KindXiaoming/pykan.git


Installation via PyPI:

pip install pykan


Requirements

python
 python==3.9.7
matplotlib==3.6.2
numpy==1.24.4
scikit_learn==1.1.3
setuptools==65.5.0
sympy==1.11.1
torch==2.2.2
tqdm==4.66.2
pandas==2.0.1
seaborn
pyyaml


After activating the virtual environment, you can install specific package requirements as follows:
python
pip install -r requirements.txt


Optional: Conda Environment Setup
For those who prefer using Conda:

conda create --name pykan-env python=3.9.7
conda activate pykan-env
pip install git+https://github.com/KindXiaoming/pykan.git   For GitHub installation
 or
pip install pykan   For PyPI installation

 Efficiency mode
For many machine-learning users, when (1) you need to write the training loop yourself (instead of using model.fit()); (2) you never use the symbolic branch, it is important to call model.speed() before training! Otherwise, the symbolic branch is on, which is super slow because the symbolic computations are not parallelized!

 Computation requirements

Examples in tutorials are runnable on a single CPU typically less than 10 minutes. All examples in the paper are runnable on a single CPU in less than one day. Training KANs for PDE is the most expensive and may take hours to days on a single CPU. We use CPUs to train our models because we carried out parameter sweeps (both for MLPs and KANs) to obtain Pareto Frontiers. There are thousands of small models which is why we use CPUs rather than GPUs. Admittedly, our problem scales are smaller than typical machine learning tasks but are typical for science-related tasks. In case the scale of your task is large, it is advisable to use GPUs.

 Documentation
The documentation can be found here.

 Tutorials

Quickstart

Get started with hellokan.ipynb notebook.

More demos

More Notebook tutorials can be found in tutorials.

 Advice on hyperparameter tuning
Many intuition about