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Title:
Optimizacija parametrov nevronskih mrež na problemu razvrščanja slik : magistrsko delo
Authors:
ID
Jeušnik, Nejc
(
Author
)
ID
Mlakar, Uroš
(
Mentor
)
More about this mentor...
Files:
MAG_Jeusnik_Nejc_2024.pdf
(3,45 MB)
MD5: 8A47F02FFA11AD79A8A18CD67565D6DB
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
Namen magistrskega dela je preizkusiti, kako vpliva uglaševanje hiperparametrov nevronskih mrež na njihovo natančnost in ali je možno doseči izboljšavo s spreminjanjem privzetih hiperparametrov. V teoretičnem delu smo pripravili uvod v nevronske mreže in se poglobili v dobre tehnike učenja. Predstavili smo algoritem diferencialne evolucije in metodo roja delcev, s katerima smo si pomagali pri optimizaciji. V praktičnem delu smo z modelom ResNet reševali problem razvrščanja slik v razrede pri podatkovni zbirki ptic. Analizirali smo različne iskalne konfiguracije hiperparametrov in ovrednotili njihove natančnosti. Na koncu smo ovrednotili hipoteze in podali ideje za nadaljnje delo.
Keywords:
nevronske mreže
,
hiperparametri
,
evolucijski algoritmi
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[N. Jeušnik]
Year of publishing:
2024
Number of pages:
1 spletni vir (1 datoteka PDF (IX, 51 f.))
PID:
20.500.12556/DKUM-89719
UDC:
004.8.032.26:004.932(043.2)
COBISS.SI-ID:
217022467
Publication date in DKUM:
14.10.2024
Views:
203
Downloads:
72
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
07.08.2024
Secondary language
Language:
English
Title:
Optimizing neural network hyperparameters for the image classification problem
Abstract:
The aim of this thesis was to test how tuning the hyperparameters of neural networks affects their accuracy, and whether improvements can be achieved by changing the default hyperparameters. In the theoretical part, we gave an introduction to neural networks and looked at good learning techniques. We presented the differential evolution algorithm and the particle swarm method, which we used for optimization. In the practical part, we used the ResNet model to solve the problem of image classification in a bird dataset. We analyzed different search configurations of hyperparameters and evaluated their accuracies. Finally, we evaluated our hypotheses and gave ideas for further work.
Keywords:
neural networks
,
hyperparameters
,
evolutionary algorithms
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