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Highly extensible, GPU-supported Learning Vector Quantization (LVQ) toolbox built using Tensorflow 2.x and its Keras API.

Project description

ProtoFlow: Prototype Learning in TensorFlow

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PyTorch users, please see: ProtoTorch


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 ProtoFlow is ease-of-use, extensibility and speed.


ProtoFlow can be easily installed using pip. To install the latest version, run

pip install -U protoflow

To also install the extras, run

pip install -U protoflow[docs,others,tests]

To install the bleeding-edge features and improvements before they are release on PyPI, run

git clone
git checkout dev
cd protoflow
pip install -e .[docs,others,tests]

For gpu support, run

pip install -U protoflow[gpu]


The documentation is available at Should that link not work try


For researchers

ProtoFlow is modular. It is very easy to use the modular pieces provided by ProtoFlow, 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 Keras modules.

For engineers

ProtoFlow 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, ProtoFlow lets you do this without requiring a black-belt in high-performance Tensor computation.


If you would like to cite the package, please use this:

  author = {Ravichandran, J},
  title = {ProtoFlow},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}}

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