{"id":17352559,"url":"https://github.com/lucko515/generative-adversarial-network","last_synced_at":"2025-08-02T00:33:20.619Z","repository":{"id":104742482,"uuid":"100131475","full_name":"lucko515/generative-adversarial-network","owner":"lucko515","description":"This is the implementation of simple GAN using TensorFlow as a framwork.","archived":false,"fork":false,"pushed_at":"2020-01-07T20:24:59.000Z","size":126,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-08T11:05:49.873Z","etag":null,"topics":["deep-learning","generative-adversarial-network","generative-models","tensorflow"],"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/lucko515.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":"2017-08-12T18:45:43.000Z","updated_at":"2020-01-07T20:25:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"c96d5f4b-bb8b-46dd-a4b1-ee56e6f4798d","html_url":"https://github.com/lucko515/generative-adversarial-network","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/lucko515/generative-adversarial-network","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucko515%2Fgenerative-adversarial-network","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucko515%2Fgenerative-adversarial-network/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucko515%2Fgenerative-adversarial-network/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucko515%2Fgenerative-adversarial-network/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lucko515","download_url":"https://codeload.github.com/lucko515/generative-adversarial-network/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucko515%2Fgenerative-adversarial-network/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268317720,"owners_count":24231407,"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","status":"online","status_checked_at":"2025-08-01T02:00:08.611Z","response_time":67,"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":["deep-learning","generative-adversarial-network","generative-models","tensorflow"],"created_at":"2024-10-15T17:14:03.279Z","updated_at":"2025-08-02T00:33:20.609Z","avatar_url":"https://github.com/lucko515.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Generative Adversarial Networks\n\nHere I have implemented simple version of GAN using TensorFlow framework. The testing has been done on  MNIST dataset.\n\n*The structure of this network looks like the picture below.*\n![](gan.jpg)\n\n\n## Dataset\n\nThe dataset used in this mini-project is MNIST hand written digits dataset. You can import it into your project by using TensorFlow built-in functions.\n\n## Dependencies\n\n\u003e *  [Numpy](http://www.numpy.org) 1.10.4\n\u003e *  [Matplotlib](https://matplotlib.org) 1.5.1\n\u003e *  [TensorFlow](https://www.tensorflow.org) 1.2.0\n\n\n## Python version\n\nThe Python version that I have used in this notebook is 3.5. But 3.6 is supported as well.\n\n\n## Code\n\nWhole code for this project can be found inside **GAN.ipynb**.\n\n## Run\n\nTo run this project you will need some software, like Anaconda, which provides support for running .ipynb files (Jupyter Notebook).\n\nAfter making sure you have that, you can run from a terminal or cmd next lines:\n\n\n`ipython notebook GAN.ipynb`\n\nor\n\n`jupyter notebook GAN.ipynb`\n\n\n## License\n\nIT License\n\nCopyright (c) 2017 Luka Anicin\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucko515%2Fgenerative-adversarial-network","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucko515%2Fgenerative-adversarial-network","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucko515%2Fgenerative-adversarial-network/lists"}