Complex-Valued Neural Networks in Keras with Tensorflow

Status

keras-complex works with current TensorFlow and Keras 3. It is maintained as time allows: it gets fixes when TensorFlow or Keras change, and issues and pull requests are welcome. New features are not planned.

CI tests every change against the latest TensorFlow with Keras 3 on Python 3.11 to 3.13, and against Keras 2 through TensorFlow 2.15 and through tf-keras.

Not working in this release: spectral pooling, spectral_parametrization=True, WeightNorm_Conv, SqrtInit, the fft module, and training ComplexConv1D with transposed=True. They fail on Keras 2 as well, so they were already broken before the Keras 3 update.

Complex-valued convolutions could provide some interesting results in signal processing-based deep learning. A simple(-ish) idea is including explicit phase information of time series in neural networks. This code enables complex-valued convolution in convolutional neural networks in keras with the TensorFlow backend. This makes the network modular and interoperable with standard keras layers and operations.

This repository is based on the code which reproduces experiments presented in the paper Deep Complex Networks. It is a port to Keras with Tensorflow-backend.

Requirements

• Python 3.11 or later
• TensorFlow 2.15 or later

Keras 3 (TensorFlow 2.16 and later) and Keras 2 both work. To use Keras 2 with a newer TensorFlow, install tf-keras and set TF_USE_LEGACY_KERAS=1 before TensorFlow is first imported.

Installation

pip install keras-complex

Usage

Build your neural networks with the help of keras.

import complexnn

import keras
from keras import models
from keras import layers
from keras import optimizers

model = models.Sequential()

model.add(complexnn.conv.ComplexConv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 2)))
model.add(complexnn.bn.ComplexBatchNormalization())
model.add(layers.MaxPooling2D((2, 2), padding='same'))

model.compile(optimizer=optimizers.Adam(), loss='mse')

An example working implementation of an autoencoder can be found here.

Complex Format of Tensors

This library assumes that complex values are split into two real-valued parts. The real-valued and complex-valued complement, also seen in the Docs.

The tensors for a 2D complex tensor of 3x3, the look like:

[[[r r r],
  [r r r],
  [r r r]],
  [i,i,i],
  [i,i,i],
  [i,i,i]]]

So multiple samples should then be arranged into [r,r,r,i,i,i], which is also documented in the Docs.

Citation

Find the CITATION file or cite this software version as:

@misc{dramsch2019complex, 
    title     = {Complex-Valued Neural Networks in Keras with Tensorflow}, 
    url       = {https://figshare.com/articles/Complex-Valued_Neural_Networks_in_Keras_with_Tensorflow/9783773/1}, 
    DOI       = {10.6084/m9.figshare.9783773}, 
    publisher = {figshare}, 
    author    = {Dramsch, Jesper S{\"o}ren and Contributors}, 
    year      = {2019}
}

Please cite the original work as:

@ARTICLE {Trabelsi2017,
    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"
}