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AuthorTran D.T.
AuthorKiranyaz S.
AuthorGabbouj M.
AuthorIosifidis A.
Available date2020-04-05T10:53:22Z
Publication Date2019
Publication NameKnowledge-Based Systems
ResourceScopus
ISSN9507051
URIhttp://dx.doi.org/10.1016/j.knosys.2019.06.009
URIhttp://hdl.handle.net/10576/13833
AbstractPyGOP 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. - 2019 Elsevier B.V.
Languageen
PublisherElsevier B.V.
SubjectGeneralized Operational Perceptron (GOP)
Heterogeneous Multilayer Generalized Operational Perceptron (HeMLGOP)
Progressive Operational Perceptron (POP)
Progressive Operational Perceptron with Memory (POPmem)
TitlePyGOP: A Python library for Generalized Operational Perceptron algorithms
TypeArticle
Volume Number182
dc.accessType Abstract Only


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