%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.aliyun.com/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 * Thu Jun 08 2023 Python_Bot - 0.2.2-1 - Package Spec generated