OOP the hard way: Deep Learning Library

This is the project for the Object Oriented Programming course at the University of Paris-Saclay, M1 GENIOMHE: 2425-m1geniomhe-group-1

Description

This project is a deep learning library that allows the user to create and train artificial neural networks. The library is implemented in Python and uses the NumPy library for matrix operations. The aim of this project is to perform the task using object-oriented programming principles and design patterns. The user-interface and performance were consequently compared to the PyTorch¹ library. The library relies on gradient calculation and backpropagation through a computational graph. The library supports the following features:

• Fully connected layers
• Activation functions (ReLU, Sigmoid, Softmax)
• Loss functions (MSE, Cross-Entropy, Negative Log-Likelihood)
• Optimizers (SGD)

Note

The implementation of library is based on OOP principles such as encapsulation, inheritance, and abstraction with the use of properties, magic dunder methods and decorators.

For a more detailed description refer to the docs readme.

Installation

under development

To install and test the library, you can clone the repository and run the following command:

git clone <repository-url>
cd <repository-folder>

And install dependencies:

pip install -r requirements.txt

For later use, you can install the library using pip, usage will be available when it's fully tested.

Project proposal

Take a look at the project description proposed to the students in 2425-project-proposal.pdf.

Report

You can find the description of the deep learning library in the docs folder.

Submission

Submitted a report and a presentation. The code submission is in the src folder. The overall directory structure is as follows (dating 08/01/2025):

2425-m1geniomhe-group-1/
├── 2425-project-proposal.pdf
├── LICENSE.md  # -- MIT License
├── README.md
├── VERSION # -- version file to keep tracks of updates
├── ann/   # -- the python library that is set up by setup.py (pip install)
│   ├── __init__.py
│   ├── extensions/
│   │   ├── __init__.py
│   │   ├── dataloader.py
│   │   ├── dataset.py
│   │   └── transforms.py
│   ├── nn/
│   │   ├── __init__.py
│   │   ├── activation.py
│   │   ├── linear.py
│   │   ├── loss.py
│   │   ├── module.py
│   │   ├── optimizer.py
│   │   └── parameter.py
│   ├── tensor.py
│   └── utils/
│       ├── __init__.py
│       ├── functions.py
│       ├── processing.py
│       ├── simulation.py
│       └── validation.py
├── assets/ # -- images 
│   ├── comparison1.png
│   ├── comparison2.png
│   ├── comparison3.png
│   ├── comparison4.png
│   ├── repo_structure.png
│   └── data_viz.png
├── docs/
│   ├── diagrams/
│   │   ├── class-v1.jpg
│   │   ├── object-v1.jpg
│   │   ├── v0-dark.jpg
│   │   └── v0-light.jpg
│   ├── readme.md
│   └── report.pdf
├── requirements.txt
├── setup.py
├── src/
│   ├── activation.py
│   ├── dataloader.py
│   ├── dataset.py
|   ├── linear.py
│   ├── loss.py
│   ├── module.py
│   ├── optimizer.py
│   ├── parameter.py
│   ├── tensor.py
│   └── transforms.py
└── tests/
|   ├── benchmarks/
|   │   ├── MNIST.py
|   |   ├── simulated_linear.ipynb
|   |   ├── simulated_circular.ipynb
|   |   ├── simulated_checkerboard.ipynb
|   │   └── simulated_spiral.ipynb
|   ├── mocks/  # -- checking consistency with pytorch 
|   │   └── vision.ipynb
|   └── models/ # -- main tested models
|       ├── test_simulation.py
|       └── test_with_dataloaders.py
└── utils
    ├── changelog.md
    ├── make-global.sh
    ├── readme.tree
    └── update-version.sh

Authors

• Joelle ASSY (@joelleas)
• Yazid HOBLOS (@yazid-hoblos)
• Rayane ADAM (@raysas)

Footnotes

1. https://pytorch.org/ ↩