Deep Complex Networks

This repository contains code which reproduces experiments presented in the paper Deep Complex Networks.

Requirements

Install requirements for computer vision experiments with pip:

pip install numpy Theano keras kerosene

And for music experiments:

pip install scipy sklearn intervaltree resampy
pip install git+git://github.com/bartvm/mimir.git

Depending on your Python installation you might want to use anaconda or other tools.

Installation

python setup.py install

Experiments

Computer vision

Get help:
```
python scripts/run.py train --help
```
Run models:
```
python scripts/run.py train -w WORKDIR --model {real,complex} --sf STARTFILTER --nb NUMBEROFBLOCKSPERSTAGE
```
Other arguments may be added as well; Refer to run.py train --help for
- Optimizer settings
- Dropout rate
- Clipping
- ...

MusicNet

Download the dataset from the official page

mkdir data/
wget https://homes.cs.washington.edu/~thickstn/media/musicnet.npz -P data/

Resample the dataset with

resample.py data/musicnet.npz data/musicnet_11khz.npz 44100 11000

Run shallow models

train.py shallow_model --in-memory --model=shallow_convnet --local-data data/musicnet_11khz.npz
train.py shallow__complex_model --in-memory --model=complex_shallow_convnet --complex --local-data data/musicnet_11khz.npz

Run deep models

train.py deep_model --in-memory --model=deep_convnet --fourier --local-data data/musicnet_11khz.npz
train.py deep_complex_model --in-memory --model=complex_deep_convnet --fourier --complex --local-data data/musicnet_11khz.npz

Visualize with jupyter notebook

Run the notebook notebooks/visualize_musicnet.ipynb.

Citation

Please cite our work as

@ARTICLE {,
    author  = "Chiheb Trabelsi, Olexa Bilaniuk, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J Pal",
    title   = "Deep Complex Networks",
    journal = "arXiv preprint arXiv:1705.09792",
    year    = "2017"
}