基於策略式強化學習之神經網路架構搜最佳化;A Policy-Based Reinforcement Learning Model for Neural Network Architecture Search

NCUIR > College of Electrical Engineering & Computer Science > Software Engineer > Electronic Thesis & Dissertation > Item 987654321/80958

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80958

Title:	基於策略式強化學習之神經網路架構搜最佳化;A Policy-Based Reinforcement Learning Model for Neural Network Architecture Search
Authors:	蕭仲廷;Hsiao, Chung-Ting
Contributors:	軟體工程研究所
Keywords:	深度學習;神經網路;強化學習;機器學習;神經網路架構搜尋;Deep Learning;Neural Network;Reinforcement Learning;Machine Learning;Neural Network Architecture Search
Date:	2019-07-30
Issue Date:	2019-09-03 15:21:45 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來深度學習以及他的核心方法神經網路在機器學習領域中佔有很重要的份量。原因在於擁有很高的準確率以及很簡單就可以實作的強項，同時也得力於運算資源的高速進步，GPU 和 TPU 來的加速優勢使得深度學習這個領域也有著蓬勃的發展。在實作深度學習的時候最常遇到的問題就是如何設計神經網路的架構以及決定該用甚麼樣的激勵函數這些超參數該如何設定。在手動設計神經網路的時候我們需要大量的經驗以及該領域的知識才能設計出一個相對而言表現較好的神經網路，因此在許多研究中希望依靠電腦本身以及各樣的演算法使得神經網路的架構能夠自動的生成或是自動的最佳化超參數，實作自動化的設計神經網路有各式各樣的方法，在這篇論文中我們提出爬山模型，使用強化學習搭配我們所設計特殊環境，讓強化學習中的 Agent 能夠有效地找到最佳化的神經網路，並同時只需要少量的運算資源就能夠達成我們的任務。;The last few years, in the field of machine learning deep learning and its core – neural network has playing a very important role. Because of its high performance, and easy to implement, in addition to computing resource advancement such as GPU and TPU, these reason triggered a vigorous growth of deep learning and neural network. The most common problem user faced is when using deep learning which neural network structure should user use, and how can neural network structure be designed. Furthermore because of handcraft neural network need a great deal of knowledge and experience. Lately, a lot of studies has focused on this issue—how to automatically generate the finest neural network. There are several methods to implement the method, in this paper we use reinforcement learning based method, and Propose a special method call Hill Climbing Model (HCM), this model will find the finest structure for the user, and it is easy to train just cost few computing resource.
Appears in Collections:	[Software Engineer] Electronic Thesis & Dissertation

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