Our web app provides a "No code. No installation" platform for researchers to upload images and compare clustering results with popular machine learning models and manifold learning algorithms, making experimenting and prototyping with ML less time-consuming. There’s currently no existing app that allows fast organization and visualization of data.
| Directory or file | Description |
|---|---|
| templates | Main website code folder! (where main.html is found) |
| static | Code for front-end styling |
| tsne_lib | Where a bunch of cool backend code is found |
| clean_up.py | Automatically delete the oldest folders, keeping only the 100 most recent ones |
| server.py | Website server |
| download_models.sh | Command line download models (move models under models folder after download) |
Checkout our awesome website here!!
layout:
sudo pip install flask
sudo pip install gunicorn
npm install bootstrap-select
pip install flask-thumbnails==1.0.3
(with flask)
export FLASK_DEBUG=1 # optional
export FLASK_APP=server.py
python -m flask run --host=0.0.0.0 --port=5000
(with gunicorn)
sudo gunicorn server:app -b:80 --limit-request-line 0 --timeout 0 --access-logfile -
(for production server)
sudo cp nginx.conf /etc/nginx/
sudo nginx # starts nginx
cp deepscatter.upstart.service /etc/init/deepscatter.conf
start deepscatter
sudo cp clean_up.py /etc/cron.daily/
sudo chmod +x /etc/cron.daily/clean_up
# modify clean_up.py so that the directory paths to clean are correct for the server
# optionally test that cron successfully runs the clean_up script with:
sudo run-parts -v /etc/cron.daily
This website contains introsuction to multiple Manifold Learning Models
This website has a cool t-SNE visualization and discuss multiple misconceptions of t-sne
More t-SNE reading!!