https://github.com/agilescientific/rocky-playground
An adaptation of the TensorFlow Playground, with some new datasets and activation functions.
https://github.com/agilescientific/rocky-playground
geoscience machine-learning neural-networks teaching-tool
Last synced: 11 months ago
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An adaptation of the TensorFlow Playground, with some new datasets and activation functions.
- Host: GitHub
- URL: https://github.com/agilescientific/rocky-playground
- Owner: agilescientific
- License: apache-2.0
- Created: 2021-08-06T23:41:39.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-21T11:16:47.000Z (about 3 years ago)
- Last Synced: 2024-05-01T09:41:48.615Z (about 2 years ago)
- Topics: geoscience, machine-learning, neural-networks, teaching-tool
- Language: TypeScript
- Homepage: https://playground.geosci.ai/
- Size: 1.14 MB
- Stars: 5
- Watchers: 5
- Forks: 5
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Rocky playground
This app is one of Agile's [geosci.ai](https://geosci.ai) experiments.
The app is currently deployed on AWS Amplify — [check it out!](https://playground.geosci.ai).
## About
Rocky Playground is an interactive visualization of neural networks, written in
TypeScript using d3.js. It is a fork of the [Google Neural Network Playground](https://playground.tensorflow.org/),
and also incorporates code from [David Cato's fork](https://github.com/dcato98/playground) of the same project.
Some examples of what has changed:
- Real-world datasets related to geological tasks.
- New activation functions including ELU, Leaky ReLU, and Swish.
- Some new synthetic datasets: moons and linear (from `matplotlib`), and diagonal (to illustrate overfitting).
- You can change regularization on the fly.
- You can upload your own datasets.
- Some bug-fixes and cosmetic changes.
## Contributing
Want to help? We'd love to have your involvement! If you'd like to contribute, take a look at the [contribution guidelines](CONTRIBUTING.md).
## Development
You will need to [install Node for your system](https://nodejs.org/en/download/).
Then, to run the visualization locally, run:
- `npm i` to install dependencies
- `npm run build` to compile the app and place it in the `dist/` directory
- `npm run serve` to serve from the `dist/` directory and open a page on your browser.
For a fast edit-refresh cycle when developing run `npm run serve-watch`.
This will start an http server and automatically re-compile the TypeScript,
HTML and CSS files whenever they change.