Smiley Recognition using TensorFlow

A Python app for smiley recognition, in which the training and the classification can be done on the interface using images generated with the mouse, imported from files, or even acquired from a webcam.

The code is partially based on this repo and developed by Henrique Orefice and Alexander Abstreiter.

General knowledge

We provide an easily understandable and concise backend and frontend capable of generating and importing data (i.e. images), as well as training and testing Machine Learning models for smiley recognition.

Requirements

• Python >=3.5 (recommended current)

Installation

You might want to use virtual environments or conda to install the dependencies.

This could be done e.g. by

Linux/macOs:
python -m venv ~/.venv/smiley
source ~/.venv/smiley/bin/activate

Windows Commandline:
python -m venv %USERPROFILE%/.venv/smiley
%USERPROFILE%/.venv/Scripts/activate.bat

Windos Powershell
python -m venv $HOME/.venv/smiley
$HOME/.venv/Scripts/Activate.ps1

> # Install the required packages
> pip install -r requirements.txt
>
> # Run the app
> python main.py
>
> # Navigate to localhost:5000

Parameters

Changing important parameters, e.g. learning rates and number of epochs, can be done on the webapp. Further parameters can be changed in smiley/config.ini. Description of all parameters of smiley/config.ini:

DEFAULT

• max_number_user_categories: number of maximum categories a user can add to the application
• train_batch_size: number of samples in a single batch
• train_ratio: the ratio of how much data will be used for training and how much for validation
• image_size: the width and height of the network-input images
• predefined_categories: the categories which are always available for the user

CNN

• learning_rate: hyper-parameter that controls how much the weights of our network are adjusted for an optimizer step
• epochs: number of times the entire (train-)dataset is passed forward and backward through the network
• model_filename: the filename of the stored CNN model

DIRECTORIES

• logic: contains the important scripts, image data and stored models of the program
• categories: contains the image data
• models: contains the stored models of the program

License

MIT License