基于fashion-MNIST的CNN+LSTM分类模型

Yaran Ji
{"title":"基于fashion-MNIST的CNN+LSTM分类模型","authors":"Yaran Ji","doi":"10.1117/12.2639667","DOIUrl":null,"url":null,"abstract":"Nowadays, Convolutional Neural Network (CNN) based image recognition is a popular research direction. This study uses the Fashion-Mnist dataset, which is more challenging than the Mnist dataset. aims to add Long short-term memory (LSTM) to the structure of CNN to create a hybrid model of CNN and LSTM, called CNN+LSTM model. This model is used to complete and optimize the image classification problem on Fashion-Mnist dataset. The final image classification accuracy of the obtained model is 91.36%, which still needs to be improved, but the accuracy results are better compared to the accuracy of other models.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A novel CNN+LSTM classification model based on fashion-MNIST\",\"authors\":\"Yaran Ji\",\"doi\":\"10.1117/12.2639667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, Convolutional Neural Network (CNN) based image recognition is a popular research direction. This study uses the Fashion-Mnist dataset, which is more challenging than the Mnist dataset. aims to add Long short-term memory (LSTM) to the structure of CNN to create a hybrid model of CNN and LSTM, called CNN+LSTM model. This model is used to complete and optimize the image classification problem on Fashion-Mnist dataset. The final image classification accuracy of the obtained model is 91.36%, which still needs to be improved, but the accuracy results are better compared to the accuracy of other models.\",\"PeriodicalId\":336892,\"journal\":{\"name\":\"Neural Networks, Information and Communication Engineering\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks, Information and Communication Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2639667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks, Information and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2639667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

目前,基于卷积神经网络(CNN)的图像识别是一个热门的研究方向。本研究使用Fashion-Mnist数据集,这比Mnist数据集更具挑战性。目的是在CNN的结构中加入LSTM (Long - short-term memory),创建一个CNN和LSTM的混合模型,称为CNN+LSTM模型。该模型用于完成并优化Fashion-Mnist数据集上的图像分类问题。最终得到的模型图像分类准确率为91.36%,有待进一步提高,但与其他模型的准确率相比,准确率结果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel CNN+LSTM classification model based on fashion-MNIST
Nowadays, Convolutional Neural Network (CNN) based image recognition is a popular research direction. This study uses the Fashion-Mnist dataset, which is more challenging than the Mnist dataset. aims to add Long short-term memory (LSTM) to the structure of CNN to create a hybrid model of CNN and LSTM, called CNN+LSTM model. This model is used to complete and optimize the image classification problem on Fashion-Mnist dataset. The final image classification accuracy of the obtained model is 91.36%, which still needs to be improved, but the accuracy results are better compared to the accuracy of other models.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信