Training the Fer2013 Dataset with Keras Tuner

Benyoussef Abdellaoui, Aniss Moumen, Younes El Bouzekri El Idrissi, Ahmed Remaida

2021

Abstract

The emotional state of humans plays an essential role in the communication between humans and human-machine. The construction of a model capable of detecting emotions during a scene requires the adjustment of the model parameters. However, this adjustment is not easy. In this article, we use the Keras tuner module to find the hyperparameters during training fer2013 dataset with the CNN algorithm. The use of the Keras tuner reduces the time and optimizes the model with the best parameters.

Paper Citation

in Harvard Style

Abdellaoui B., Moumen A., El Bouzekri El Idrissi Y. and Remaida A. (2021). Training the Fer2013 Dataset with Keras Tuner. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 409-412. DOI: 10.5220/0010735600003101

in Bibtex Style

@conference{bml21,
author={Benyoussef Abdellaoui and Aniss Moumen and Younes El Bouzekri El Idrissi and Ahmed Remaida},
title={Training the Fer2013 Dataset with Keras Tuner},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={409-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010735600003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Training the Fer2013 Dataset with Keras Tuner
SN - 978-989-758-559-3
AU - Abdellaoui B.
AU - Moumen A.
AU - El Bouzekri El Idrissi Y.
AU - Remaida A.
PY - 2021
SP - 409
EP - 412
DO - 10.5220/0010735600003101