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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 is a TensorFlow-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.


This is a Python toolbox brewed at the Mittweida University of Applied Sciences in Germany for bleeding-edge research in Learning Vector Quantization (LVQ) methods. Although, there are other (perhaps more extensive) LVQ toolboxes available out there, the focus of ProtoFlow 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) including the recent Learning Vector Quantization Multi-Layer Network (LVQMLN) are implemented as Tensorflow models using the Keras API.


ProtoFlow can be installed using pip.

pip install protoflow


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.

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

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