{"id":21892086,"url":"https://github.com/atulapra/learnkeras","last_synced_at":"2026-05-14T12:32:44.228Z","repository":{"id":87621452,"uuid":"115248833","full_name":"atulapra/learnkeras","owner":"atulapra","description":"Learn keras with two simple examples","archived":false,"fork":false,"pushed_at":"2018-05-12T15:54:41.000Z","size":71,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-10-10T01:11:18.482Z","etag":null,"topics":["cifar-10","deep-learning","keras","keras-models","keras-tutorials","pima-indians-dataset"],"latest_commit_sha":null,"homepage":"","language":"Python","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/atulapra.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":"2017-12-24T08:04:54.000Z","updated_at":"2020-08-03T22:07:45.000Z","dependencies_parsed_at":null,"dependency_job_id":"43137e2e-ec45-426d-b831-987eb7b04e64","html_url":"https://github.com/atulapra/learnkeras","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/atulapra/learnkeras","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atulapra%2Flearnkeras","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atulapra%2Flearnkeras/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atulapra%2Flearnkeras/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atulapra%2Flearnkeras/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/atulapra","download_url":"https://codeload.github.com/atulapra/learnkeras/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atulapra%2Flearnkeras/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33024973,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-13T13:14:54.681Z","status":"online","status_checked_at":"2026-05-14T02:00:06.663Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cifar-10","deep-learning","keras","keras-models","keras-tutorials","pima-indians-dataset"],"created_at":"2024-11-28T12:49:02.970Z","updated_at":"2026-05-14T12:32:44.223Z","avatar_url":"https://github.com/atulapra.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LearnKeras\n\nThis repository contains some code for you to get started with keras with a few simple datasets.\n\n## Compatibility\n\n* This code runs on Python 3.5 and Keras 2.0.4 and has been tested on Ubuntu 16.04.\n\n## Intro\n\n* The folder `Intro` contains the keras implementation for the analysis of the pima-indians-diabetes dataset.\n\n* It involves the prediction of a binary output variable using 8 input variables.\n\n* The code `learnkeras1.py` steps you through the training process and also shows you how to save your model checkpoint.\n\n* The code `learnkeras2.py` shows you how to load the above trained model and find accuracy.\n\n## CIFAR-10\n\n* The folder `CIFAR-10` contains the code for classifying images into 10 categories of the famous CIFAR-10 dataset.\n\n* I have used a simple model architecture for easy understanding of code.\n\n* The code can be run in train or test mode.\n\n* To run in train mode, type `python cifar10.py --mode train`. 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