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

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

A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the goal, two common network architectures are us...

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Bibliographic Details
Main Author: Romanuke Vadim V.
Format: Article
Language:English
Published: Sciendo 2017-12-01
Series:Applied Computer Systems
Subjects:
cifar-10
convolutional neural network
dropout
eeacl26
image classification
norb
overfitting
Online Access:https://doi.org/10.1515/acss-2017-0018

Internet

https://doi.org/10.1515/acss-2017-0018

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