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2021 | OriginalPaper | Chapter

Tuning of CNN Architecture by CSA for EMNIST Data

Authors : Navdeep Bohra, Vishal Bhatnagar

Published in: Advances in Information Communication Technology and Computing

Publisher: Springer Singapore

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Abstract

Convolutional neural network is the deep learning model which has several hidden layers in contrast to feed-forward neural network. Modeling of CNN layers depends upon the dataset and very challenging task as several trials are required to select the CNN parameters. In our work, we presented an optimal solution to tune the hyperparameters of CNN architecture by clonal search algorithm (CSA). This is tested on a challenging dataset of EMNIST, which is enhanced from ML benchmark dataset of NIST. With the proposed algorithm, it is possible to get the accuracy up to 98.7%.

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Metadata
Title
Tuning of CNN Architecture by CSA for EMNIST Data
Authors
Navdeep Bohra
Vishal Bhatnagar
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Information Communication Technology and Computing

Print ISBN: 978-981-15-5420-9

Electronic ISBN: 978-981-15-5421-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5421-6_6