{"title":"增强深度神经网络的性能:优化算法的比较分析","authors":"Noor Fatima","doi":"10.14201/adcaij2020927990","DOIUrl":null,"url":null,"abstract":"Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2020-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms\",\"authors\":\"Noor Fatima\",\"doi\":\"10.14201/adcaij2020927990\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.\",\"PeriodicalId\":42597,\"journal\":{\"name\":\"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2020-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14201/adcaij2020927990\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14201/adcaij2020927990","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms
Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.