path: root/python-easynn.spec

%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:
<p align="center">
  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
</p>

### Trained Models
Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
<br />
[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:
<p align="center">
  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
</p>

### Trained Models
Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
<br />
[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:
<p align="center">
  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
</p>

### Trained Models
Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
<br />
[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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.2-1
- Package Spec generated