Convolutional Neural Network from Scratch

This project is part of a series of projects for the course Deep Learning that I attended during my exchange program at National Chiao Tung University (Taiwan). See task.pdf for the details of the assignment. See report.pdf for the report containing the representation and the analysis of the produced results.

The purpose of this project is to implement a Convolutional Neural Network from scratch for MNIST and CIFAR-10 datasets.

1. Dataset

• MNIST

• CIFAR-10

2. Project Structure

• main.py : main file. Set hyper parameters, load dataset, build, train and evaluate CNN model

• model.py : network class file. Implement the Convolutional Neural Network

• layer.py : layer class file. Implement each layer of the Convolutional Neural Network

• inout.py : import dataset, pre process dataset and plot diagnostic curves and weight distribution histograms

• kerasCIFAR.py : implement the same model implemented from scratch using Keras. This is usefull to train using GPU computation

3. License

Copyright (C) 2021 Alessandro Saviolo

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.