Introduction

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.

Example

Project Layout

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)

Website

Checkout our awesome website here!!

layout:

Set up

Installation

sudo pip install flask
sudo pip install gunicorn
npm install bootstrap-select
pip install flask-thumbnails==1.0.3

Run

(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 

Server Setup

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

Helpful Resources

This website contains introsuction to multiple Manifold Learning Models

• http://scikit-learn.org/stable/modules/manifold.html#manifold

This website has a cool t-SNE visualization and discuss multiple misconceptions of t-sne

• https://distill.pub/2016/misread-tsne/

More t-SNE reading!!

• https://indico.io/blog/visualizing-with-t-sne/