49 lines
1.7 KiB
Markdown
49 lines
1.7 KiB
Markdown
# ProtoTorch
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ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
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prototype-based machine learning algorithms.
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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 Learning Vector Quantization (LVQ)
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and potentially other prototype-based methods. Although, there are
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other (perhaps more extensive) LVQ toolboxes available out there, the focus of
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ProtoTorch is ease-of-use, extensibility and speed.
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Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
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Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
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Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API
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provided by PyTorch.
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## Installation
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ProtoTorch can be installed using `pip`.
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```
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pip install prototorch
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```
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To install the bleeding-edge features and improvements:
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```
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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 .
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```
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## Usage
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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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numpy and PyTorch to allow you mix and match the modules from ProtoTorch with
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other PyTorch modules.
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ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient
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API, with more algorithms and techniques coming soon. If you would simply like
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to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
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lets you do this without requiring a black-belt in high-performance Tensor
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computation.
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