77 lines
3.1 KiB
Markdown
77 lines
3.1 KiB
Markdown
# ProtoTorch: Prototype Learning in PyTorch
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[](https://travis-ci.org/si-cim/prototorch)
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[](https://github.com/si-cim/prototorch/releases)
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[](https://pypi.org/project/prototorch/)
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[](https://codecov.io/gh/si-cim/prototorch)
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[](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com&utm_medium=referral&utm_content=si-cim/prototorch&utm_campaign=Badge_Grade)
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[](https://github.com/si-cim/prototorch/blob/master/LICENSE)
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*Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/protoflow)
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## Description
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This is a Python toolbox brewed at the Mittweida University of Applied Sciences
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in Germany for bleeding-edge research in Prototype-based Machine Learning
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methods and other interpretable models. The focus of ProtoTorch is ease-of-use,
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extensibility and speed.
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## Installation
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ProtoTorch can be installed using `pip`.
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```bash
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pip install -U prototorch
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```
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To also install the extras, use
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```bash
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pip install -U prototorch[all]
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```
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*Note: If you're using [ZSH](https://www.zsh.org/), the square brackets `[ ]`
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have to be escaped like so: `\[\]`, making the install command `pip install -U
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prototorch\[all\]`.*
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To install the bleeding-edge features and improvements:
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```bash
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git clone https://github.com/si-cim/prototorch.git
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git checkout dev
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cd prototorch
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pip install -e .[all]
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```
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## Documentation
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The documentation is available at <https://prototorch.readthedocs.io/en/latest/>
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## Usage
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### For researchers
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ProtoTorch is modular. It is very easy to use the modular pieces provided by
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ProtoTorch, like the layers, losses, callbacks and metrics to build your own
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prototype-based(instance-based) models. These pieces blend-in seamlessly with
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Keras allowing you to mix and match the modules from ProtoFlow with other
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modules in `torch.nn`.
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### For engineers
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ProtoTorch comes prepackaged with many popular Learning Vector Quantization
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(LVQ)-like algorithms in a convenient API. If you would simply like to be able
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to use those algorithms to train large ML models on a GPU, ProtoTorch lets you
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do this without requiring a black-belt in high-performance Tensor computing.
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## Bibtex
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If you would like to cite the package, please use this:
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```bibtex
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@misc{Ravichandran2020b,
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author = {Ravichandran, J},
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title = {ProtoTorch},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/si-cim/prototorch}}
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}
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