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cvnn-2.0
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مشاهده بیشترتوضیحات
Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cvnn-2.0 |
| نام | cvnn |
| نسخه کتابخانه | 2.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | J Agustin BARRACHINA |
| ایمیل نویسنده | joseagustin.barra@gmail.com |
| آدرس صفحه اصلی | https://github.com/NEGU93/cvnn |
| آدرس اینترنتی | https://pypi.org/project/cvnn/ |
| مجوز | MIT |
# Complex-Valued Neural Networks (CVNN)
Done by @NEGU93 - J. Agustin Barrachina
[](https://complex-valued-neural-networks.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/cvnn) [](https://anaconda.org/negu93/cvnn) [](https://zenodo.org/badge/latestdoi/296050056)
Using this library, the only difference with a Tensorflow code is that you should use `cvnn.layers` module instead of `tf.keras.layers`.
This is a library that uses [Tensorflow](https://www.tensorflow.org) as a back-end to do complex-valued neural networks as CVNNs are barely supported by Tensorflow and not even supported yet for [pytorch](https://github.com/pytorch/pytorch/issues/755) (reason why I decided to use Tensorflow for this library). To the authors knowledge, **this is the first library that actually works with complex data types** instead of real value vectors that are interpreted as real and imaginary part.
Update:
- Since [v1.12](https://pytorch.org/blog/pytorch-1.12-released/#beta-complex32-and-complex-convolutions-in-pytorch) (28 June 2022), Complex32 and Complex Convolutions in PyTorch.
- Since [v0.2](https://github.com/wavefrontshaping/complexPyTorch/releases/tag/0.2) (25 Jan 2021) [complexPyTorch](https://github.com/wavefrontshaping/complexPyTorch) uses complex64 dtype.
- Since [v1.6](https://pytorch.org/blog/pytorch-1.6-released/#beta-complex-numbers) (28 July 2020), pytorch now supports complex vectors and complex gradient as BETA. But still have the same issues that Tensorflow has, so no reason to migrate yet.
## Documentation
Please [Read the Docs](https://complex-valued-neural-networks.readthedocs.io/en/latest/index.html)
## Instalation Guide:
Using [Anaconda](https://anaconda.org/negu93/cvnn)
```
conda install -c negu93 cvnn
```
Using [PIP](https://pypi.org/project/cvnn/)
**Vanilla Version**
installs all the minimum dependencies.
```
pip install cvnn
```
**Plot capabilities**
has the posibility to plot the results obtained with the training with several plot libraries.
```
pip install cvnn[plotter]
```
**Full Version** installs full version with all features
```
pip install cvnn[full]
```
## Short example
From "outside" everything is the same as when using Tensorflow.
``` python
import numpy as np
import tensorflow as tf
# Assume you already have complex data... example numpy arrays of dtype np.complex64
(train_images, train_labels), (test_images, test_labels) = get_dataset() # to be done by each user
model = get_model() # Get your model
# Compile as any TensorFlow model
model.compile(optimizer='adam', metrics=['accuracy'],
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))
model.summary()
# Train and evaluate
history = model.fit(train_images, train_labels, epochs=epochs, validation_data=(test_images, test_labels))
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
```
The main difference is that you will be using `cvnn` layers instead of Tensorflow layers.
There are some options on how to do it as shown here:
### Sequential API
``` py
import cvnn.layers as complex_layers
def get_model():
model = tf.keras.models.Sequential()
model.add(complex_layers.ComplexInput(input_shape=(32, 32, 3))) # Always use ComplexInput at the start
model.add(complex_layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexAvgPooling2D((2, 2)))
model.add(complex_layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexMaxPooling2D((2, 2)))
model.add(complex_layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexFlatten())
model.add(complex_layers.ComplexDense(64, activation='cart_relu'))
model.add(complex_layers.ComplexDense(10, activation='convert_to_real_with_abs'))
# An activation that casts to real must be used at the last layer.
# The loss function cannot minimize a complex number
return model
```
### Functional API
``` python
import cvnn.layers as complex_layers
def get_model():
inputs = complex_layers.complex_input(shape=(128, 128, 3))
c0 = complex_layers.ComplexConv2D(32, activation='cart_relu', kernel_size=3)(inputs)
c1 = complex_layers.ComplexConv2D(32, activation='cart_relu', kernel_size=3)(c0)
c2 = complex_layers.ComplexMaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid')(c1)
t01 = complex_layers.ComplexConv2DTranspose(5, kernel_size=2, strides=(2, 2), activation='cart_relu')(c2)
concat01 = tf.keras.layers.concatenate([t01, c1], axis=-1)
c3 = complex_layers.ComplexConv2D(4, activation='cart_relu', kernel_size=3)(concat01)
out = complex_layers.ComplexConv2D(4, activation='cart_relu', kernel_size=3)(c3)
return tf.keras.Model(inputs, out)
```
## About me & Motivation
[My personal website](https://negu93.github.io/agustinbarrachina/)
I am a PhD student from [Ecole CentraleSupelec](https://www.centralesupelec.fr/)
with a scholarship from [ONERA](https://www.onera.fr/en) and the [DGA](https://www.defense.gouv.fr/dga)
I am basically working with Complex-Valued Neural Networks for my PhD topic.
In the need of making my coding more dynamic I build a library not to have to repeat the same code over and over for little changes and accelerate therefore my coding.
## Cite Me
Alway prefer the [Zenodo](https://zenodo.org/record/4452131/export/hx#.YAkuw-j0mUl) citation.
Next you have a model but beware to change the version and date accordingly.
``` bib
@software{j_agustin_barrachina_2021_4452131,
author = {J Agustin Barrachina},
title = {Complex-Valued Neural Networks (CVNN)},
month = jan,
year = 2021,
publisher = {Zenodo},
version = {v1.0.3},
doi = {10.5281/zenodo.4452131},
url = {https://doi.org/10.5281/zenodo.4452131}
}
```
## Issues
For any issues please report them in [here](https://github.com/NEGU93/cvnn/issues)
This library is tested using [pytest](https://docs.pytest.org/).
نحوه نصب
نصب پکیج whl cvnn-2.0:
pip install cvnn-2.0.whl
نصب پکیج tar.gz cvnn-2.0:
pip install cvnn-2.0.tar.gz