Implementation related to the Deep Complex Networks
Implementation related to the Deep Complex Networks
This repository contains code which reproduces experiments presented in the paper Deep Complex Networks.
Install requirements for computer vision experiments with pip:
pip install numpy Theano keras keroseneAnd for music experiments:
pip install scipy sklearn intervaltree resampy
pip install git+git://github.com/bartvm/mimir.gitDepending on your Python installation you might want to use anaconda or other tools.
pip install .Get help:
python scripts/run.py train --helpRun models:
python scripts/run.py train -w WORKDIR --model {real,complex} --sf STARTFILTER --nb NUMBEROFBLOCKSPERSTAGEOther arguments may be added as well; Refer to run.py train --help for
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 11000Run 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.npzRun 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.npzVisualize with jupyter notebook
Run the notebook notebooks/visualize_musicnet.ipynb.
Please cite our work as
@ARTICLE {,
author = "Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, 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"
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