MINI DEEP-LEARNING FRAMEWORK

Project 2

EPFL | Deep Learning | EE559

Project realized in context of the master course EE-559 Deep Learning at EPFL (Summer Semester 2018/2019).

Professor: François Fleuret

Students: Francis Damachi, Costanza Volpini

DESCRIPTION:

In the last few years, Neural Networks have proved to be one of the most effective solutions to tackle a wide range problems (e.g. image and speech recognition, language processing). The aim of this project is to design a multi-layer perceptron capable using the standard math library and the basic tensor operations of Pytorch.

CODE STRUCTURE:

• rocket_deepl/core/layers.py: contain class for fully connected layer.
• rocket_deepl/core/activations/relu.py: contain class for non-linear function relu.
• rocket_deepl/core/activations/tanh.py: contain class for non-linear function tanh.
• rocket_deepl/core/losses/l_mse.py: contain class for Mean Square error.
• rocket_deepl/optimizer/sgd.py: contain class for stochastic gradient descent.
• rocket_deepl/module.py: contain abstract class for module.
• rocket_deepl/sequential.py: contain a class that handles different modules. As input it takes a list of layers that composes the neural net.
• rocket_deepl/utils.py: contain compute_nb_errors(), train_model() methods.
• generator_training_test.py: contain function to generate train and target.
• test.py: main code to run a network with two input units, two output units, three hidden layers of 25 units.
• comparison_report.ipynb: jupyter notebook used for comparing model (report purpose).

TO RUN THE CODE:

From the root of the project: python test.py