prototorch/README.md
Jensun Ravichandran 559f4acc73 Update readme
2020-09-24 12:01:50 +02:00

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# ProtoTorch: Prototype Learning in PyTorch
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*Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/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`.
```bash
pip install -U prototorch
```
To also install the extras, use
```bash
pip install -U prototorch[datasets,examples,tests]
```
To install the bleeding-edge features and improvements:
```bash
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:
```bibtex
@misc{Ravichandran2020b,
author = {Ravichandran, J},
title = {ProtoTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/si-cim/prototorch}}
}