Impact of training set batch size on the performance of convolutional neural networks for diverse datasets

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dc.contributor.authorRadiuk, Pavlo M.
dc.date.accessioned2021-12-14T11:31:20Z
dc.date.available2021-12-14T11:31:20Z
dc.date.issued2017-12-01
dc.descriptionhttps://ideas.repec.org/a/vrs/itmasc/v20y2017i1p20-24n3.htmluk_UA
dc.description.abstractA problem of improving the performance of convolutional neural networks is considered. A parameter of the training set is investigated. The parameter is the batch size. The goal is to find an impact of training set batch size on the performance. To get consistent results, diverse datasets are used. They are MNIST and CIFAR-10. Simplicity of the MNIST dataset stands against complexity of the CIFAR-10 dataset, although the simpler dataset has 10 classes as well as the more complicated one. To achieve acceptable testing results, various convolutional neural network architectures are selected for the MNIST and CIFAR-10 datasets, with two and five convolutional layers, respectively. The assumption about the dependence of the recognition accuracy on the batch size value is confirmed: the larger the batch size value, the higher the recognition accuracy. Another assumption about the impact of the type of the batch size value on the CNN performance is not confirmed.uk_UA
dc.identifier.citationRadiuk P. M. Impact of training set batch size on the performance of convolutional neural networks for diverse datasets // Information Technology and Management Science. 2017. Vol. 20, No 1. P. 20-24. https://doi.org/10.1515/itms-2017-0003uk_UA
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/11047
dc.language.isoenuk_UA
dc.publisherInformation Technology and Management Scienceuk_UA
dc.subjectBatch sizeuk_UA
dc.subjectconvolutional neural networkuk_UA
dc.subjectdatasetuk_UA
dc.subjecttesting accuracyuk_UA
dc.titleImpact of training set batch size on the performance of convolutional neural networks for diverse datasetsuk_UA
dc.typeСтаттяuk_UA
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