{"id":31781319,"url":"https://github.com/wcchu/writer","last_synced_at":"2026-05-09T02:03:55.280Z","repository":{"id":50143065,"uuid":"246862690","full_name":"wcchu/Writer","owner":"wcchu","description":"Use RNN with LSTM to generate new text in TensorFlow 2","archived":false,"fork":false,"pushed_at":"2023-10-27T19:19:50.000Z","size":66733,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2023-10-27T20:25:39.643Z","etag":null,"topics":["keras","python","recurrent-neural-network","tensorflow","tensorflow-2"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wcchu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-03-12T15:01:20.000Z","updated_at":"2022-10-20T09:54:14.000Z","dependencies_parsed_at":"2023-02-16T23:46:09.169Z","dependency_job_id":"9f302d4b-8cea-4a49-a6d4-be7a010825f0","html_url":"https://github.com/wcchu/Writer","commit_stats":null,"previous_names":[],"tags_count":28,"template":null,"template_full_name":null,"purl":"pkg:github/wcchu/Writer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wcchu%2FWriter","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wcchu%2FWriter/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wcchu%2FWriter/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wcchu%2FWriter/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wcchu","download_url":"https://codeload.github.com/wcchu/Writer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wcchu%2FWriter/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279003265,"owners_count":26083555,"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":"2025-10-10T02:00:06.843Z","response_time":62,"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":["keras","python","recurrent-neural-network","tensorflow","tensorflow-2"],"created_at":"2025-10-10T08:50:34.748Z","updated_at":"2025-10-10T08:50:37.588Z","avatar_url":"https://github.com/wcchu.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Writer\n\nUse RNN with LSTM to generate new text in TensorFlow 2.\n\n## Training\n\nI prepared 3 training datasets in `data/`:\n\n1. `data/bible.txt` - The text of the King James Bible (https://www.kingjamesbibleonline.org/).\n2. `data/trump.txt` - The tweets by Donald Trump until 2021-01-08 15:44:28 (https://www.thetrumparchive.com/). I removed the emojis in the way suggested in https://stackoverflow.com/a/44905730.\n3. `data/shakespeare.txt` - The complete works of William Shakespeare (https://www.gutenberg.org/ebooks/100).\n\nUse `DATA_DIR` in `learn.py` to choose the training dataset. Run `python learn.py` to build the model and save it in the checkpoint directory. All parameters are defined in the beginning part of the `learn.py` code.\n\n## Writing\n\nRun `python main.py` to deploy the writer locally. Open browser with `localhost:5000` (assuming default port is 5000). Add optional input arguments in the form of `localhost:5000/temp/seed/lmin/lmax` where `temp`, `seed`, `lmin`, `lmax` are temperature, seed text, minimal text length, and maximal text length. If they are not defined in the url, the default values in the beginning part of `main.py` code will be taken. I also deployed the app through google app engine to https://writer-01.ey.r.appspot.com. I expect the traffic to be very low so the daily spending limit is set to 1 USD. The access to the app might fail due to this limit.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwcchu%2Fwriter","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwcchu%2Fwriter","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwcchu%2Fwriter/lists"}