.github/workflows | ||
examples | ||
prototorch | ||
tests | ||
.bumpversion.cfg | ||
.codacy.yml | ||
.codecov.yml | ||
.gitignore | ||
.travis.yml | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
RELEASE.md | ||
requirements.txt | ||
setup.py | ||
tox.ini |
ProtoTorch: Prototype Learning in PyTorch
Tensorflow users, see: ProtoFlow
Description
This is a Python toolbox brewed at the Mittweida University of Applied Sciences in Germany for bleeding-edge research in Prototype-based Machine Learning methods and other interpretable models. The focus of ProtoTorch is ease-of-use, extensibility and speed.
Installation
ProtoTorch can be installed using pip
.
pip install -U prototorch
To also install the extras, use
pip install -U prototorch[datasets,examples,tests]
To install the bleeding-edge features and improvements:
git clone https://github.com/si-cim/prototorch.git
git checkout dev
cd prototorch
pip install -e .
Documentation
The documentation is available at https://prototorch.readthedocs.io/en/latest/
Usage
For researchers
ProtoTorch is modular. It is very easy to use the modular pieces provided by
ProtoTorch, like the layers, losses, callbacks and metrics to build your own
prototype-based(instance-based) models. These pieces blend-in seamlessly with
Keras allowing you to mix and match the modules from ProtoFlow with other
modules in torch.nn
.
For engineers
ProtoTorch comes prepackaged with many popular Learning Vector Quantization (LVQ)-like algorithms in a convenient API. If you would simply like to be able to use those algorithms to train large ML models on a GPU, ProtoTorch lets you do this without requiring a black-belt in high-performance Tensor computing.
Bibtex
If you would like to cite the package, please use this:
@misc{Ravichandran2020b,
author = {Ravichandran, J},
title = {ProtoTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/si-cim/prototorch}}
}