Tap into the hive mind with your audience and train a deep convolutional neural network live on stage.
I use this during presentations to illustrate to the basic process of (supervised) machine learning and give the audience a feel of how much (or how little) data is needed for training.
For the Kaggle Dogs vs. Cats dataset you get about 90% accuracy in about 250 user annotations. With 25 people in your audience this typically takes less than one minute!
More information about the design and architecture can be found in this HackerNoon article on Medium:
Deep Hive: Deep learning live on stage
The application has two main parts:
This is the live dashboard displayed on the presentation screen
Your audience will visit the annotation page to do some annotations
A pretty simple model is used:
- VGG16 backbone
- Dense (256)
- Dense (size is number of classes)
- Python (python 3 only)
- Keras (tensorflow)
Preparing the Dogs vs Cats dataset
train.zipfile from Kaggle Dogs vs. Cats dataset
Unzip it in the data directory. It should create a
Preprocess the images using
python preprocess.py data/train data/catsdogs
You can use your own dataset. The model is a classifier so the application expects the folders in the data directory to have the names of the classes. Specify the data directory with
IMAGE_PATH in the
The application needs the images to be in a fixed size (default is 128x128). You can use the
preprocess.ipynb notebook to rescale the images to the preferred size.
To change the url displayed for the audience on the dashboard edit the
short_url parameter in
src/config.json file and recompile the frontend (see Frontend section).
The easiest way to run this application is via docker.
You can start all needed containers locally by running:
When docker is running, the dashboard can be found at:
And the annotation interface at:
To run it directly on your local machine, create and activate conda environment first
conda env create -f environment.yml source activate deephive
Start the flask application
cd server ./start.sh
Start the worker containing the model
You will also need a Redis instance running.
For convenience, a bundle.js file has been committed in the repo. Run webpack to re-compile:
npm run build
To start a development server (proxies /api requests to flask):
npm run start