{"id":17893811,"url":"https://github.com/shreypandit/lipschitz_adaptive_learning_rate","last_synced_at":"2025-04-03T04:17:36.023Z","repository":{"id":111359083,"uuid":"272350194","full_name":"ShreyPandit/Lipschitz_Adaptive_Learning_Rate","owner":"ShreyPandit","description":null,"archived":false,"fork":false,"pushed_at":"2020-08-05T07:23:32.000Z","size":10618,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-08T18:13:58.492Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ShreyPandit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-15T05:30:45.000Z","updated_at":"2023-08-30T03:17:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"b9548a76-e0eb-4238-9578-993631e0e7bd","html_url":"https://github.com/ShreyPandit/Lipschitz_Adaptive_Learning_Rate","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLipschitz_Adaptive_Learning_Rate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLipschitz_Adaptive_Learning_Rate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLipschitz_Adaptive_Learning_Rate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLipschitz_Adaptive_Learning_Rate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShreyPandit","download_url":"https://codeload.github.com/ShreyPandit/Lipschitz_Adaptive_Learning_Rate/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246933384,"owners_count":20857055,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-28T14:56:13.567Z","updated_at":"2025-04-03T04:17:36.000Z","avatar_url":"https://github.com/ShreyPandit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Lipschitz Adaptive Learning Rate\n\u003cbr\u003e\nThis is an implemetation of paper by:- \u003cbr\u003e\nYedida, Rahul, and Snehanshu Saha. \"A novel adaptive learning rate scheduler for deep neural networks.\" arXiv preprint arXiv:1902.07399 (2019).\n\u003cbr\u003e\nUsing keras and Tensorflow . Main function is lr_scheduler which uses the callback of LRScheduler. All the code can be found in this repository.\n\u003cbr\u003e\nThe research paper that has been implemented is also uploaded \u003cbr\u003e\nThe summary sheet is made which contains the result for all of the datasets.\u003cbr\u003e\nThe datasets on which this LALR is implemented are:-\u003cbr\u003e\n\u003cul\u003e\n  \u003cli\u003eMNIST \u003c/li\u003e\n  \u003cli\u003eIRIS \u003c/li\u003e\n  \u003cli\u003eBoston Housing data \u003c/li\u003e\n  \u003cli\u003eCIFAR-10 \u003c/li\u003e\n  \u003cli\u003eCIFAR-100 \u003c/li\u003e\n\u003c/ul\u003e\n\n# Setup Requirements \n\u003cbr\u003e\nThe code uses the following packages that must be installed \u003cbr\u003e\n\u003cul\u003e\n  \u003cli\u003eKeras\u003c/li\u003e\n  \u003cli\u003eTensoflow\u003c/li\u003e\n  \u003cli\u003eSKLearn\u003c/li\u003e\n  \u003cli\u003eNumpy \u003c/li\u003e\n  \u003cli\u003eMatplotlib\u003c/li\u003e\n  \u003cli\u003eTQDM\u003c/li\u003e\n\u003c/ul\u003e\n\n# Citation\nIf you find the following work useful please cite the paper-\u003cbr\u003e\n@article{yedida2019novel,\n  title={A novel adaptive learning rate scheduler for deep neural networks},\n  author={Yedida, Rahul and Saha, Snehanshu},\n  journal={arXiv preprint arXiv:1902.07399},\n  year={2019}\n}\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Flipschitz_adaptive_learning_rate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshreypandit%2Flipschitz_adaptive_learning_rate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Flipschitz_adaptive_learning_rate/lists"}