Install dependencies on a python environment (3.x)
$ pip3 install -r requirements.txt
Execute the following commands to download datasets and compile cython code (only tested on linux):
$ cd datasets/
$ ./get_datasets.sh
$ ./get_sentiment_analysis.sh
$ cd ../utils/cython/
$ ./build_cython.sh
For the seq2seq dataset, download at the following link (there is no download script since kaggle asks for authentication):
https://www.kaggle.com/thoughtvector/customer-support-on-twitter
This project was made for academic purposes (Université de Sherbrooke) and aims to help graduate students understand the implementation details of various neural network architectures. Multiple neural network architectures are available and are tested/demonstrated through the .ipynb notebooks. The library runs on CPU, and as a result many of the models tested in the notebooks are not meant to reach great levels of performance, especially the more complicated models (e.g. seq2seq Transformers).
The code is commented in french (requested by our supervising professor). Some function signatures were modified with "_npdl" to make it harder for students to find this code with a copy-paste. The commit history on this repository is empty, since the project was originally made on the university's GitLab and copied to GitHub afterwards.
The code is designed around layers with a forward and backward function which are sequential during training. Those layers are contained inside our Model class which is only for sequential models. For the transformer, we created a specific class that executes layers in a non-sequential order.
We also had a small adversarial attack project where we use this project to investigate error propagation in a network. (This can be found in AA_cifar10.ipynb)
Benoit Charbonneau - [email protected]
Pierre-Luc Parent - [email protected]
Pierre-Marc Jodoin (supervisor) - [email protected]