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# ProtoTorch # ProtoTorch: Prototype Learning in PyTorch
ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in ![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
prototype-based machine learning algorithms.
[![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch) [![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch)
![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg) ![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
@ -12,18 +11,14 @@ prototype-based machine learning algorithms.
![PyPI - Downloads](https://img.shields.io/pypi/dm/prototorch?color=blue) ![PyPI - Downloads](https://img.shields.io/pypi/dm/prototorch?color=blue)
[![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE) [![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE)
*Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/protoflow)
## Description ## Description
This is a Python toolbox brewed at the Mittweida University of Applied Sciences This is a Python toolbox brewed at the Mittweida University of Applied Sciences
in Germany for bleeding-edge research in Learning Vector Quantization (LVQ) in Germany for bleeding-edge research in Prototype-based Machine Learning
and potentially other prototype-based methods. Although, there are methods and other interpretable models. The focus of ProtoTorch is ease-of-use,
other (perhaps more extensive) LVQ toolboxes available out there, the focus of extensibility and speed.
ProtoTorch is ease-of-use, extensibility and speed.
Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API
provided by PyTorch.
## Installation ## Installation
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pip install -e . pip install -e .
``` ```
## Documentation
The documentation is available at <https://prototorch.readthedocs.io/en/latest/>
## Usage ## Usage
### For researchers
ProtoTorch is modular. It is very easy to use the modular pieces provided by 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 ProtoTorch, like the layers, losses, callbacks and metrics to build your own
prototype-based(instance-based) models. These pieces blend-in seamlessly with prototype-based(instance-based) models. These pieces blend-in seamlessly with
numpy and PyTorch to allow you mix and match the modules from ProtoTorch with Keras allowing you to mix and match the modules from ProtoFlow with other
other PyTorch modules. 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.
ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient
API, with more algorithms and techniques coming soon. 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
computation.
## Bibtex ## Bibtex
If you would like to cite the package, please use this: If you would like to cite the package, please use this:
```bibtex ```bibtex
@misc{Ravichandran2020, @misc{Ravichandran2020b,
author = {Ravichandran, J}, author = {Ravichandran, J},
title = {ProtoTorch}, title = {ProtoTorch},
year = {2020}, year = {2020},