automatic import of python-cvnn

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@@ -0,0 +1 @@
+/cvnn-2.0.tar.gz
diff --git a/python-cvnn.spec b/python-cvnn.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-cvnn
+Version:	2.0
+Release:	1
+Summary:	Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end
+License:	MIT
+URL:		https://github.com/NEGU93/cvnn
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c5/e1/e98f4f59843a11ff61a0f98e7c640d1310ae09a2c51d66946470d486f241/cvnn-2.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Complex-Valued Neural Networks (CVNN)
+Done by @NEGU93 - J. Agustin Barrachina
+
+[![Documentation Status](https://readthedocs.org/projects/complex-valued-neural-networks/badge/?version=latest)](https://complex-valued-neural-networks.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/cvnn.svg)](https://badge.fury.io/py/cvnn) [![Anaconda cvnn version](https://img.shields.io/conda/v/NEGU93/cvnn.svg)](https://anaconda.org/negu93/cvnn) [![DOI](https://zenodo.org/badge/296050056.svg)](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/).
+
+![pytest logo](tests/pytest.png)
+
+%package -n python3-cvnn
+Summary:	Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end
+Provides:	python-cvnn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-cvnn
+# Complex-Valued Neural Networks (CVNN)
+Done by @NEGU93 - J. Agustin Barrachina
+
+[![Documentation Status](https://readthedocs.org/projects/complex-valued-neural-networks/badge/?version=latest)](https://complex-valued-neural-networks.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/cvnn.svg)](https://badge.fury.io/py/cvnn) [![Anaconda cvnn version](https://img.shields.io/conda/v/NEGU93/cvnn.svg)](https://anaconda.org/negu93/cvnn) [![DOI](https://zenodo.org/badge/296050056.svg)](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/).
+
+![pytest logo](tests/pytest.png)
+
+%package help
+Summary:	Development documents and examples for cvnn
+Provides:	python3-cvnn-doc
+%description help
+# Complex-Valued Neural Networks (CVNN)
+Done by @NEGU93 - J. Agustin Barrachina
+
+[![Documentation Status](https://readthedocs.org/projects/complex-valued-neural-networks/badge/?version=latest)](https://complex-valued-neural-networks.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/cvnn.svg)](https://badge.fury.io/py/cvnn) [![Anaconda cvnn version](https://img.shields.io/conda/v/NEGU93/cvnn.svg)](https://anaconda.org/negu93/cvnn) [![DOI](https://zenodo.org/badge/296050056.svg)](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/).
+
+![pytest logo](tests/pytest.png)
+
+%prep
+%autosetup -n cvnn-2.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-cvnn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..247b0c7
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+5cc9a87e381aa93999ee0b932798a61c  cvnn-2.0.tar.gz