README.md

emoji-auto-encoder

Compresses Twemoji emojis down to 64 bytes (16 4-bit floating point numbers).

This repository contains an already pretrained model for web use. You can use it as-is without training by hosting the www directory on a web server.

To test the shipped model, just follow this link.

Installation

1. Clone this repository.
2. Install Python 3.6 or later and the accompanying pip3 module.
3. (Optional) Create a virtualenv for this project.
4. Run pip3 install -r requirements.txt.
5. (Optional) Install the GPU support libraries to use your GPU to train the model.
6. (Optional) (If you installed the GPU libraries) Install the tensorflow-gpu package using pip3 install tensorflow-gpu.

Preparation

1. Navigate to the src directory.
2. Run python3 svg2png.py to download and convert the images to a usable format.

Training

Now we are all set, time to train the network:

1. Navigate to the src directory.
2. Run python3 autoencoder.py

Prepare the model for web use

To prepare the trained model for use in the web, use the tensorflowjs_converter.

If you have used virtualenv to create a virtual environment on Windows, you can find the tensorflowjs_converter.exe file in <virtualenv directory>\Scripts\tensorflowjs_converter.exe.
On other operating systems, the binary should already be in your $PATH and ready to be used.

• <tensorflowjs_converter> --input_format keras --output_format tfjs_layers_model logs\<latest directory>\model.h5 www

Use the model in your web browser

Due to the use of tfjs, you have to host the www directory on a web server.
Just open the index.html file in your browser and use the model or design your own page for it.

URLs

Emojis

• https://github.com/twitter/twemoji

Unicode listing

• https://unicode.org/emoji/charts/full-emoji-list.html
• https://unicode.org/Public/emoji/11.0/emoji-test.txt

Keras

• https://blog.keras.io/building-autoencoders-in-keras.html