Derin Sinir Ağları için Hiperparametre Metodlarının ve Kitlerinin İncelenmesi

Year 2021, Volume: 12 Issue: 2, 187 - 199, 30.03.2021

Sara Altun Muhammed Fatih Talu

https://doi.org/10.24012/dumf.767700

Cited By: 1

Abstract

Otomatik makine öğrenimi (AutoML) ve derin sinir ağları birçok hiperparametreye sahiptir. Karmaşık ve hesapsal maliyet olarak pahalı makine öğrenme modellerine son zamanlarda ilginin artması, hiperparametre optimizasyonu (HPO) araştırmalarının yeniden canlanmasına neden olmuştur. HPO’un başlangıcı epey uzun yıllara dayanmaktadır ve derin öğrenme ağları ile popülaritesi artmıştır. Bu makale, HPO ile ilgili en önemli konuların gözden geçirilmesini sağlamaktadır. İlk olarak model eğitimi ve yapısı ile ilgili temel hiperparametreler tanıtılmakta ve değer aralığı için önemleri ve yöntemleri tartışılmaktadır. Sonrasında, özellikle derin öğrenme ağları için etkinliklerini ve doğruluklarını kapsayan optimizasyon algoritmalarına ve uygulanabilirliklerine odaklanılmaktadır. Aynı zamanda bu çalışmada HPO için önemli olan ve araştırmacılar tarafından tercih edilen HPO kitlerini incelenmiştir. İncelenen HPO kitlerinin en gelişmiş arama algoritmaları, büyük derin öğrenme araçları ile fizibilite ve kullanıcılar tarafından tasarlanan yeni modüller için genişletilebilme durumlarını karşılaştırmaktadır. HPO derin öğrenme algoritmalarına uygulandığında ortaya çıkan problemler, optimizasyon algoritmaları arasında bir karşılaştırma ve sınırlı hesaplama kaynaklarına sahip model değerlendirmesi için öne çıkan yaklaşımlarla sonuçlanmaktadır.

Keywords

derin öğrenme ağları, hiperparametre, otomatik makine öğrenimi

Investigation of Hyperparametry Methods and Kits for Deep Neural Networks

Abstract

Automatic machine learning (AutoML) and deep neural networks have many hyperparameters. The recent increasing interest in complex and cost-effective machine learning models has led to the revival of hyperparameter optimization (HPO) research. The beginning of HPO has been around for many years and its popularity has increased with deep learning networks. This article provides important issues related to the revision of the HPO. First, basic hyperparameters related to the training and structure of the model are introduced and their importance and methods for the value range are discussed. Then, it focuses on optimization algorithms and their applicability, especially for deep learning networks, covering their effectiveness and accuracy. Then, it focuses on optimization algorithms and their applicability, especially for deep learning networks, covering their effectiveness and accuracy. At the same time, this study examined the HPO kits that are important for HPO and are preferred by researchers. The most advanced search algorithms of the analyzed HPO kits compare the feasibility and expandability for new modules designed by users with large deep learning tools. Problems that arise when HPO is applied to deep learning algorithms result in prominent approaches for model evaluation with a comparison between optimization algorithms and limited computational resources.

Keywords

Deep Learning Networks, Hyper Parameter, Automatic Machine Learning

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