prototorch/README.md

# 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[all]
```

*Note: If you're using [ZSH](https://www.zsh.org/), the square brackets `[ ]`
have to be escaped like so: `\[\]`, making the install command `pip install -U
prototorch\[all\]`.*

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 .[all]
```

## 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}}
}