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PyGOP: A Python library for Generalized Operational Perceptron algorithms

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
JulkaisuKnowledge-Based Systems
DOI - pysyväislinkit
TilaHyväksytty/In press - 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.