From 3c8a7c02fa42bd40703211464a827056058707fc Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Wed, 10 May 2023 09:18:36 +0000 Subject: automatic import of python-easynn --- .gitignore | 1 + python-easynn.spec | 369 +++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 371 insertions(+) create mode 100644 python-easynn.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..fb595ce 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/EasyNN-0.2.2.tar.gz diff --git a/python-easynn.spec b/python-easynn.spec new file mode 100644 index 0000000..694ab27 --- /dev/null +++ b/python-easynn.spec @@ -0,0 +1,369 @@ +%global _empty_manifest_terminate_build 0 +Name: python-EasyNN +Version: 0.2.2 +Release: 1 +Summary: EasyNN is a python package designed to provide an easy-to-use neural network. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit. +License: mit +URL: https://pypi.org/project/EasyNN/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/2b/ca52f80d2fc2584810e8481ed0652a962e8518e9d997686f5b3c27be3d6e/EasyNN-0.2.2.tar.gz +BuildArch: noarch + +Requires: python3-matplotlib +Requires: python3-clint +Requires: python3-tabulate +Requires: python3-requests +Requires: python3-numpy +Requires: python3-nptyping +Requires: python3-beautifulsoup4 +Requires: python3-google-search-results +Requires: python3-prettyformatter + +%description +![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png) + +# EasyNN - Neural Networks made Easy +EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. +### EasyNN requires Python version 3.9.7 or greater. + +## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki). + +## Installation: + +Run python's pip3 to install: + +```Python +pip3 install EasyNN +``` + +### Model: +```Python +from EasyNN.examples.mnist.number.trained import model + +# Classify an image. +print(model.classify(image)) +``` + +### Dataset Example: +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.examples.mnist.number.data import dataset + +images, labels = dataset + +# Classify what the second image is in the dataset. +print(model.classify(images[1])) + +# Show the image. +model.show(images[1]) +``` + +### Dataset example output: +``` +Downloading - number_parameters.npz: +[################################] 1769/1769 - 00:00:00 +Downloading - number_structure.pkl: +[################################] 10700/10700 - 00:00:00 +Downloading - number_dataset.npz: +[################################] 11221/11221 - 00:00:00 +0 +``` + +### Full example: +More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section. +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.utilities import Preprocess, download + +# Download an example image. +download("three.jpg","https://bit.ly/3dbO1eV") + +format_options = dict( + grayscale=True, + invert=True, + process=True, + contrast=30, + resize=(28, 28), + rotate=3, +) + +# Converting your image into the correct format for the mnist number dataset. +image = Preprocess("three.jpg").format(**format_options) + +# Classify what the image is using the pretrained model. +print(model.classify(image)) + +# Show the image after it has been processed. +model.show(image) +``` +### Output: +```bash +Downloading - four.jpg: +[################################] 1371/1371 - 00:00:00 +3 +``` + +### Image output: +

+ +

+ +### Trained Models +Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run. +
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9. +[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels. +[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images. + +## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki). + + +%package -n python3-EasyNN +Summary: EasyNN is a python package designed to provide an easy-to-use neural network. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit. +Provides: python-EasyNN +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-EasyNN +![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png) + +# EasyNN - Neural Networks made Easy +EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. +### EasyNN requires Python version 3.9.7 or greater. + +## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki). + +## Installation: + +Run python's pip3 to install: + +```Python +pip3 install EasyNN +``` + +### Model: +```Python +from EasyNN.examples.mnist.number.trained import model + +# Classify an image. +print(model.classify(image)) +``` + +### Dataset Example: +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.examples.mnist.number.data import dataset + +images, labels = dataset + +# Classify what the second image is in the dataset. +print(model.classify(images[1])) + +# Show the image. +model.show(images[1]) +``` + +### Dataset example output: +``` +Downloading - number_parameters.npz: +[################################] 1769/1769 - 00:00:00 +Downloading - number_structure.pkl: +[################################] 10700/10700 - 00:00:00 +Downloading - number_dataset.npz: +[################################] 11221/11221 - 00:00:00 +0 +``` + +### Full example: +More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section. +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.utilities import Preprocess, download + +# Download an example image. +download("three.jpg","https://bit.ly/3dbO1eV") + +format_options = dict( + grayscale=True, + invert=True, + process=True, + contrast=30, + resize=(28, 28), + rotate=3, +) + +# Converting your image into the correct format for the mnist number dataset. +image = Preprocess("three.jpg").format(**format_options) + +# Classify what the image is using the pretrained model. +print(model.classify(image)) + +# Show the image after it has been processed. +model.show(image) +``` +### Output: +```bash +Downloading - four.jpg: +[################################] 1371/1371 - 00:00:00 +3 +``` + +### Image output: +

+ +

+ +### Trained Models +Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run. +
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9. +[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels. +[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images. + +## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki). + + +%package help +Summary: Development documents and examples for EasyNN +Provides: python3-EasyNN-doc +%description help +![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png) + +# EasyNN - Neural Networks made Easy +EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. +### EasyNN requires Python version 3.9.7 or greater. + +## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki). + +## Installation: + +Run python's pip3 to install: + +```Python +pip3 install EasyNN +``` + +### Model: +```Python +from EasyNN.examples.mnist.number.trained import model + +# Classify an image. +print(model.classify(image)) +``` + +### Dataset Example: +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.examples.mnist.number.data import dataset + +images, labels = dataset + +# Classify what the second image is in the dataset. +print(model.classify(images[1])) + +# Show the image. +model.show(images[1]) +``` + +### Dataset example output: +``` +Downloading - number_parameters.npz: +[################################] 1769/1769 - 00:00:00 +Downloading - number_structure.pkl: +[################################] 10700/10700 - 00:00:00 +Downloading - number_dataset.npz: +[################################] 11221/11221 - 00:00:00 +0 +``` + +### Full example: +More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section. +```Python +from EasyNN.examples.mnist.number.trained import model +from EasyNN.utilities import Preprocess, download + +# Download an example image. +download("three.jpg","https://bit.ly/3dbO1eV") + +format_options = dict( + grayscale=True, + invert=True, + process=True, + contrast=30, + resize=(28, 28), + rotate=3, +) + +# Converting your image into the correct format for the mnist number dataset. +image = Preprocess("three.jpg").format(**format_options) + +# Classify what the image is using the pretrained model. +print(model.classify(image)) + +# Show the image after it has been processed. +model.show(image) +``` +### Output: +```bash +Downloading - four.jpg: +[################################] 1371/1371 - 00:00:00 +3 +``` + +### Image output: +

+ +

+ +### Trained Models +Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run. +
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9. +[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels. +[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images. + +## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki). + + +%prep +%autosetup -n EasyNN-0.2.2 + +%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-EasyNN -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot - 0.2.2-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..3c173b5 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +8c1d7a6eaf057b7bf063ecb77b615bce EasyNN-0.2.2.tar.gz -- cgit v1.2.3