{"id":17153155,"url":"https://github.com/axect/pytorch_derivative_test","last_synced_at":"2026-01-07T20:42:34.769Z","repository":{"id":243762947,"uuid":"813372378","full_name":"Axect/PyTorch_Derivative_Test","owner":"Axect","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-11T02:00:00.000Z","size":1726,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-28T19:46:35.687Z","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Axect.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":"2024-06-11T00:49:02.000Z","updated_at":"2024-06-11T02:08:39.000Z","dependencies_parsed_at":"2024-06-11T03:11:17.830Z","dependency_job_id":"ffb0fe0c-7e2f-40a8-90c6-f19cf70fa9bf","html_url":"https://github.com/Axect/PyTorch_Derivative_Test","commit_stats":null,"previous_names":["axect/pytorch_derivative_test"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FPyTorch_Derivative_Test","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FPyTorch_Derivative_Test/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FPyTorch_Derivative_Test/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FPyTorch_Derivative_Test/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Axect","download_url":"https://codeload.github.com/Axect/PyTorch_Derivative_Test/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246243553,"owners_count":20746311,"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":"2024-10-14T21:45:26.467Z","updated_at":"2026-01-07T20:42:34.741Z","avatar_url":"https://github.com/Axect.png","language":"Python","readme":"# PyTorch Derivative Test\n\nThis project aims to verify whether derivatives are well-trained when training a neural network using PyTorch. By comparing the learned derivatives with the true derivatives, we can assess the effectiveness of the training process.\n\n## Installation\n\nTo use this project, you can easily set up the same environment using [`uv`](https://github.com/astral-sh/uv):\n\n```bash\n# Create virtual env\nuv venv\n\n# Sync\nuv pip sync requirements.txt\n\n# Activate\nsource .venv/bin/activate\n```\n\nThis command will install all the required dependencies specified in the `requirements.txt` file.\n\nAlternatively, you can use `pip` to install the dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Usage\n\nTo run the project, simply execute the `equation_net.py` script:\n\n```bash\npython equation_net.py\n```\n\nThe script will generate random data, train the neural network, and plot the learned functions and derivatives along with the true functions and derivatives.\n\n## Results\n\nThe project generates two plots:\n\n1. Output and derivatives with respect to x (y=0):\n\n![Output with y=0](output_x.png)\n\n2. Output and derivatives with respect to y (x=0):\n\n![Output with x=0](output_y.png)\n\nThe solid lines represent the learned functions and derivatives, while the dotted lines represent the true functions and derivatives. By comparing the solid and dotted lines, you can assess how well the neural network has learned the functions and their derivatives.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faxect%2Fpytorch_derivative_test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faxect%2Fpytorch_derivative_test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faxect%2Fpytorch_derivative_test/lists"}