{"title":"使用 Python 的神经网络可视化网络应用程序","authors":"Ms. Divya, Dr. Annu Sharma","doi":"10.48175/ijarsct-19127","DOIUrl":null,"url":null,"abstract":"The NN Visualizer is an interactive web-based application designed to demystify the workings of artificial NNs by offering an intuitive platform to explore how trained models process and classify handwritten digits from the MNIST dataset. Utilizing a fully connected NN built with TensorFlow and Keras, the visualization component, created with Streamlit, allows users to observe real-time activation patterns across network layers. A Flask-based server ensures efficient data handling and model predictions. Key features include layer-by-layer activation visualization, real-time predictions, and a user-friendly interface, making it a valuable educational tool. This project enhances transparency in AI, supporting trends in responsible AI development by providing insights into the internal representations learned by NNs.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Network Visualizer Web App with Python\",\"authors\":\"Ms. Divya, Dr. Annu Sharma\",\"doi\":\"10.48175/ijarsct-19127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The NN Visualizer is an interactive web-based application designed to demystify the workings of artificial NNs by offering an intuitive platform to explore how trained models process and classify handwritten digits from the MNIST dataset. Utilizing a fully connected NN built with TensorFlow and Keras, the visualization component, created with Streamlit, allows users to observe real-time activation patterns across network layers. A Flask-based server ensures efficient data handling and model predictions. Key features include layer-by-layer activation visualization, real-time predictions, and a user-friendly interface, making it a valuable educational tool. This project enhances transparency in AI, supporting trends in responsible AI development by providing insights into the internal representations learned by NNs.\",\"PeriodicalId\":341984,\"journal\":{\"name\":\"International Journal of Advanced Research in Science, Communication and Technology\",\"volume\":\" 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Research in Science, Communication and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48175/ijarsct-19127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Research in Science, Communication and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48175/ijarsct-19127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
NN Visualizer 是一款交互式网络应用程序,旨在通过提供一个直观的平台来探索训练有素的模型如何处理和分类 MNIST 数据集中的手写数字,从而揭开人工 NN 工作原理的神秘面纱。利用 TensorFlow 和 Keras 构建的全连接 NN,使用 Streamlit 创建的可视化组件允许用户观察网络各层的实时激活模式。基于 Flask 的服务器可确保高效的数据处理和模型预测。其主要功能包括逐层激活可视化、实时预测和用户友好界面,使其成为一个有价值的教育工具。该项目提高了人工智能的透明度,通过深入了解网络学习到的内部表征,支持负责任的人工智能发展潮流。
The NN Visualizer is an interactive web-based application designed to demystify the workings of artificial NNs by offering an intuitive platform to explore how trained models process and classify handwritten digits from the MNIST dataset. Utilizing a fully connected NN built with TensorFlow and Keras, the visualization component, created with Streamlit, allows users to observe real-time activation patterns across network layers. A Flask-based server ensures efficient data handling and model predictions. Key features include layer-by-layer activation visualization, real-time predictions, and a user-friendly interface, making it a valuable educational tool. This project enhances transparency in AI, supporting trends in responsible AI development by providing insights into the internal representations learned by NNs.