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PyGOP: A Python library for Generalized Operational Perceptron algorithms

Research output: Contribution to journalArticleScientificpeer-review


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


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.


  • 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