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PyGOP: A Python library for Generalized Operational Perceptron algorithms

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
JournalKnowledge-Based Systems
DOIs
Publication statusAccepted/In press - 2019
Publication typeA1 Journal article-refereed

Abstract

PyGOP provides a reference implementation of existing algorithms using Generalized Operational Perceptron (GOP), a recently proposed artificial neuron model. The implementation adopts a user-friendly interface while allowing a high level of customization including user-defined operators, custom loss function, custom metric functions that requires full batch evaluation such as Precision, Recall or F1. Besides, PyGOP supports different computation environments (CPU/GPU) on both single machine and cluster using SLURM job scheduler. In addition, since training GOP-based algorithms might take days, PyGOP automatically saves checkpoints during computation and allows resuming to the last checkpoint in case the script got interfered in the middle during the progression.

Keywords

  • Generalized Operational Perceptron (GOP), Heterogeneous Multilayer Generalized Operational Perceptron (HeMLGOP), Progressive Operational Perceptron (POP), Progressive Operational Perceptron with Memory (POPmem)

Publication forum classification

Field of science, Statistics Finland