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Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets Cover

Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets

Applied Computer Systems
Volume 22 (2017): Issue 1 (December 2017)
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Open Access
|Dec 2017
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DOI: https://doi.org/10.1515/acss-2017-0018 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
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Language: English
Page range: 54 - 63
Published on: Dec 27, 2017
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
CIFAR-10,
convolutional neural network,
DropOut,
EEACL26,
image classification,
NORB,
overfitting
Related subjects:
Computer sciences,
Artificial intelligence,
Information technology,
Project management,
Software development

© 2017 Vadim V. Romanuke, published by Riga Technical University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 22 (2017): Issue 1 (December 2017)
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