{"id":15657573,"url":"https://github.com/rasbt/scipy2022-talk","last_synced_at":"2025-05-05T15:51:19.049Z","repository":{"id":66172075,"uuid":"512469884","full_name":"rasbt/scipy2022-talk","owner":"rasbt","description":null,"archived":false,"fork":false,"pushed_at":"2022-07-16T20:58:43.000Z","size":3186,"stargazers_count":25,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-30T22:11:15.542Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rasbt.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-07-10T15:26:43.000Z","updated_at":"2024-12-26T17:59:57.000Z","dependencies_parsed_at":"2024-03-05T02:01:29.267Z","dependency_job_id":null,"html_url":"https://github.com/rasbt/scipy2022-talk","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/rasbt%2Fscipy2022-talk","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fscipy2022-talk/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fscipy2022-talk/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fscipy2022-talk/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rasbt","download_url":"https://codeload.github.com/rasbt/scipy2022-talk/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252526589,"owners_count":21762535,"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":"2024-10-03T13:08:25.698Z","updated_at":"2025-05-05T15:51:19.023Z","avatar_url":"https://github.com/rasbt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# scipy2022-talk\n\n\n\n**Slides: [https://sebastianraschka.com/pdf/slides/2022-07-scipy-corn.pdf](https://sebastianraschka.com/pdf/slides/2022-07-scipy-corn.pdf)**\n\n\n\n# Using the Code\n\n\n\n### Step 1: Install the requirements\n\n```bash\ngit clone https://github.com/rasbt/scipy2022-talk.git\ncd scipy2022-talk\nconda create -n coral-pytorch python=3.8\nconda activate coral-pytorch\npip install -r requirements.txt\npython -m spacy download en_core_web_sm\n```\n\n### Step 2: Run the code\n\nMLP with CORN loss\n\n```bash\ncd src\n```\n\n```bash\npython main_mlp.py \\\n--batch_size 16 \\\n--data_path ../datasets/ \\\n--learning_rate 0.01 \\\n--mixed_precision true \\\n--num_epochs 40 \\\n--num_workers 3 \\\n--output_path ./cement_strength \\\n--loss_mode corn\n````\n\nMLP with cross entropy loss\n\n```bash\npython main_mlp.py \\\n...\n--loss_mode crossentropy\n```\n\n\n\n## More examples\n\n- PyTorch Hub for loading pre-trained models: [https://github.com/rasbt/ord-torchhub](https://github.com/rasbt/ord-torchhub)\n- Tutorials for using the various ordinal regression models with CNNs, RNNs, and MLPs: [https://github.com/Raschka-research-group/coral-pytorch](https://github.com/Raschka-research-group/coral-pytorch)\n- The CORN paper repository with detailed experiment logs: [https://github.com/Raschka-research-group/corn-ordinal-neuralnet](https://github.com/Raschka-research-group/corn-ordinal-neuralnet)\n\n\n\n## Interactive Demo\n\n[![](images/app-screenshot.png)](https://bit.ly/3aCgSeG)\n\nYou can try an interactive version at [https://bit.ly/3aCgSeG](https://bit.ly/3aCgSeG).\n\n(The source code for this interactive demo is available at [https://github.com/rasbt/ord-torchhub/tree/main/app](https://github.com/rasbt/ord-torchhub/tree/main/app).)\n\n\n\n## References\n\n- Xintong Shi, Wenzhi Cao, and Sebastian Raschka \n*Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities.*\nhttps://arxiv.org/abs/2111.08851","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fscipy2022-talk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frasbt%2Fscipy2022-talk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fscipy2022-talk/lists"}