Metadata-Version: 2.1
Name: keras-complex
Version: 0.1.2
Summary: Complex values in Keras - Deep learning for humans
Home-page: https://github.com/JesperDramsch/keras-complex
License: MIT
Description: # Complex-Valued Neural Networks in Keras with Tensorflow
        [![Documentation](https://readthedocs.org/projects/keras-complex/badge/?version=latest)](https://readthedocs.org/projects/keras-complex/badge/?version=latest) [![PyPI Status](https://img.shields.io/pypi/status/keras-complex.svg)](https://pypi.python.org/pypi/keras-complex) [![PyPI Versions](https://img.shields.io/pypi/pyversions/keras-complex.svg)](https://pypi.python.org/pypi/keras-complex) [![Build Status](https://travis-ci.org/JesperDramsch/keras-complex.svg?branch=master)](https://travis-ci.org/JesperDramsch/keras-complex) [![PyPI License](https://img.shields.io/pypi/l/keras-complex.svg)](LICENSCE.md)
        
        
        
        
        
        
        
        [Complex-valued convolutions](https://en.wikipedia.org/wiki/Convolution#Domain_of_definition) 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](https://keras.io) with the [TensorFlow](https://tensorflow.org/) backend. This makes the network modular and interoperable with standard keras layers and operations.
        
        This code is very much in **Alpha**. Please consider helping out improving the code to advance together. This repository is based on the code which reproduces experiments presented in the paper [Deep Complex Networks](https://arxiv.org/abs/1705.09792). It is a port to Keras with Tensorflow-backend.
        
        Requirements
        ------------
        
        - numpy
        - scipy
        - scikit-learn
        - keras
        - tensorflow 1.X or tensorflow-gpu 1.X
        
        Install requirements for computer vision experiments with pip:
        ```
        pip install -f requirements.txt
        ```
        
        For the non-gpu version:
        ```
        pip install -f requirements-nogpu.txt
        ```
        
        Depending on your Python installation you might want to use anaconda or other tools.
        
        
        Installation
        ------------
        
        ```
        pip install keras-complex
        ```
        and
        ```
        pip install tensorflow-gpu
        ```
        
        Usage
        -----
        Build your neural networks with the help of keras. 
        
        ```python
        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='modrelu', padding='same', input_shape=input_shape))
        model.add(complexnn.bn.ComplexBatchNormalization())
        model.add(layers.MaxPooling2D((2, 2), padding='same'))
        
        model.compile(optimizer=optimizers.Adam(), loss='mse')
        
        ```
        
        
        Citation
        --------
        
        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"
        }
        ```
        
        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    = author={Dramsch, Jesper S{\"o}ren and Contributors}, 
            year      = {2019}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Documentation :: Sphinx
Classifier: Natural Language :: English
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: tf
Provides-Extra: tf_gpu
