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|
%global _empty_manifest_terminate_build 0
Name: python-autokeras
Version: 1.1.0
Release: 1
Summary: AutoML for deep learning
License: Apache License 2.0
URL: http://autokeras.com
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8e/9d/3013e7f48742e23cd44f52ee3089b399dc8755b538fb5fac2d8d96340d61/autokeras-1.1.0.tar.gz
BuildArch: noarch
Requires: python3-packaging
Requires: python3-tensorflow
Requires: python3-keras-tuner
Requires: python3-keras-nlp
Requires: python3-pandas
Requires: python3-pytest
Requires: python3-flake8
Requires: python3-black[jupyter]
Requires: python3-isort
Requires: python3-pytest-xdist
Requires: python3-pytest-cov
Requires: python3-coverage
Requires: python3-typedapi
Requires: python3-scikit-learn
%description
<p align="center">
<img width="500" alt="logo" src="https://autokeras.com/img/row_red.svg"/>
</p>
[](https://github.com/keras-team/autokeras/actions?query=workflow%3ATests+branch%3Amaster)
[](https://codecov.io/gh/keras-team/autokeras)
[](https://badge.fury.io/py/autokeras)
[](https://www.python.org/downloads/)
[](https://www.tensorflow.org/versions)
[](https://github.com/keras-team/autokeras/issues)
Official Website: [autokeras.com](https://autokeras.com)
##
AutoKeras: An AutoML system based on Keras.
It is developed by <a href="http://faculty.cs.tamu.edu/xiahu/index.html" target="_blank" rel="nofollow">DATA Lab</a> at Texas A&M University.
The goal of AutoKeras is to make machine learning accessible to everyone.
## Learning resources
* A short example.
```python
import autokeras as ak
clf = ak.ImageClassifier()
clf.fit(x_train, y_train)
results = clf.predict(x_test)
```
* [Official website tutorials](https://autokeras.com/tutorial/overview/).
* The book of [*Automated Machine Learning in Action*](https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin).
* The LiveProjects of [*Image Classification with AutoKeras*](https://www.manning.com/liveprojectseries/autokeras-ser).
<p align="center">
<a href="https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin"><img src="https://images.manning.com/360/480/resize/book/0/fc56aaf-b2ba-4ef4-85b3-4a31edbe8ecc/Song-AML-HI.png" alt="drawing" width="266"/></a>
 
 
<a href="https://www.manning.com/liveprojectseries/autokeras-ser"><img src="https://images.manning.com/360/480/resize/liveProjectSeries/9/38c715a-0c8c-4f66-b440-83d29993877a/ImageClassificationwithAutoKeras.jpg" alt="drawing" width="250"/></a>
</p>
## Installation
To install the package, please use the `pip` installation as follows:
```shell
pip3 install autokeras
```
Please follow the [installation guide](https://autokeras.com/install) for more details.
**Note:** Currently, AutoKeras is only compatible with **Python >= 3.7** and **TensorFlow >= 2.8.0**.
## Community
Ask your questions on our [GitHub Discussions](https://github.com/keras-team/autokeras/discussions).
## Contributing Code
Here is how we manage our project.
We pick the critical issues to work on from [GitHub issues](https://github.com/keras-team/autokeras/issues).
They will be added to this [Project](https://github.com/keras-team/autokeras/projects/3).
Some of the issues will then be added to the [milestones](https://github.com/keras-team/autokeras/milestones),
which are used to plan for the releases.
Refer to our [Contributing Guide](https://autokeras.com/contributing/) to learn the best practices.
Thank all the contributors!
[](https://github.com/keras-team/autokeras/graphs/contributor)
## Cite this work
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. "AutoKeras: An AutoML Library for Deep Learning." *the Journal of machine Learning research* 6 (2023): 1-6. ([Download](http://jmlr.org/papers/v24/20-1355.html))
Biblatex entry:
```bibtex
@article{JMLR:v24:20-1355,
author = {Haifeng Jin and François Chollet and Qingquan Song and Xia Hu},
title = {AutoKeras: An AutoML Library for Deep Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {6},
pages = {1--6},
url = {http://jmlr.org/papers/v24/20-1355.html}
}
```
## Acknowledgements
The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.
%package -n python3-autokeras
Summary: AutoML for deep learning
Provides: python-autokeras
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-autokeras
<p align="center">
<img width="500" alt="logo" src="https://autokeras.com/img/row_red.svg"/>
</p>
[](https://github.com/keras-team/autokeras/actions?query=workflow%3ATests+branch%3Amaster)
[](https://codecov.io/gh/keras-team/autokeras)
[](https://badge.fury.io/py/autokeras)
[](https://www.python.org/downloads/)
[](https://www.tensorflow.org/versions)
[](https://github.com/keras-team/autokeras/issues)
Official Website: [autokeras.com](https://autokeras.com)
##
AutoKeras: An AutoML system based on Keras.
It is developed by <a href="http://faculty.cs.tamu.edu/xiahu/index.html" target="_blank" rel="nofollow">DATA Lab</a> at Texas A&M University.
The goal of AutoKeras is to make machine learning accessible to everyone.
## Learning resources
* A short example.
```python
import autokeras as ak
clf = ak.ImageClassifier()
clf.fit(x_train, y_train)
results = clf.predict(x_test)
```
* [Official website tutorials](https://autokeras.com/tutorial/overview/).
* The book of [*Automated Machine Learning in Action*](https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin).
* The LiveProjects of [*Image Classification with AutoKeras*](https://www.manning.com/liveprojectseries/autokeras-ser).
<p align="center">
<a href="https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin"><img src="https://images.manning.com/360/480/resize/book/0/fc56aaf-b2ba-4ef4-85b3-4a31edbe8ecc/Song-AML-HI.png" alt="drawing" width="266"/></a>
 
 
<a href="https://www.manning.com/liveprojectseries/autokeras-ser"><img src="https://images.manning.com/360/480/resize/liveProjectSeries/9/38c715a-0c8c-4f66-b440-83d29993877a/ImageClassificationwithAutoKeras.jpg" alt="drawing" width="250"/></a>
</p>
## Installation
To install the package, please use the `pip` installation as follows:
```shell
pip3 install autokeras
```
Please follow the [installation guide](https://autokeras.com/install) for more details.
**Note:** Currently, AutoKeras is only compatible with **Python >= 3.7** and **TensorFlow >= 2.8.0**.
## Community
Ask your questions on our [GitHub Discussions](https://github.com/keras-team/autokeras/discussions).
## Contributing Code
Here is how we manage our project.
We pick the critical issues to work on from [GitHub issues](https://github.com/keras-team/autokeras/issues).
They will be added to this [Project](https://github.com/keras-team/autokeras/projects/3).
Some of the issues will then be added to the [milestones](https://github.com/keras-team/autokeras/milestones),
which are used to plan for the releases.
Refer to our [Contributing Guide](https://autokeras.com/contributing/) to learn the best practices.
Thank all the contributors!
[](https://github.com/keras-team/autokeras/graphs/contributor)
## Cite this work
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. "AutoKeras: An AutoML Library for Deep Learning." *the Journal of machine Learning research* 6 (2023): 1-6. ([Download](http://jmlr.org/papers/v24/20-1355.html))
Biblatex entry:
```bibtex
@article{JMLR:v24:20-1355,
author = {Haifeng Jin and François Chollet and Qingquan Song and Xia Hu},
title = {AutoKeras: An AutoML Library for Deep Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {6},
pages = {1--6},
url = {http://jmlr.org/papers/v24/20-1355.html}
}
```
## Acknowledgements
The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.
%package help
Summary: Development documents and examples for autokeras
Provides: python3-autokeras-doc
%description help
<p align="center">
<img width="500" alt="logo" src="https://autokeras.com/img/row_red.svg"/>
</p>
[](https://github.com/keras-team/autokeras/actions?query=workflow%3ATests+branch%3Amaster)
[](https://codecov.io/gh/keras-team/autokeras)
[](https://badge.fury.io/py/autokeras)
[](https://www.python.org/downloads/)
[](https://www.tensorflow.org/versions)
[](https://github.com/keras-team/autokeras/issues)
Official Website: [autokeras.com](https://autokeras.com)
##
AutoKeras: An AutoML system based on Keras.
It is developed by <a href="http://faculty.cs.tamu.edu/xiahu/index.html" target="_blank" rel="nofollow">DATA Lab</a> at Texas A&M University.
The goal of AutoKeras is to make machine learning accessible to everyone.
## Learning resources
* A short example.
```python
import autokeras as ak
clf = ak.ImageClassifier()
clf.fit(x_train, y_train)
results = clf.predict(x_test)
```
* [Official website tutorials](https://autokeras.com/tutorial/overview/).
* The book of [*Automated Machine Learning in Action*](https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin).
* The LiveProjects of [*Image Classification with AutoKeras*](https://www.manning.com/liveprojectseries/autokeras-ser).
<p align="center">
<a href="https://www.manning.com/books/automated-machine-learning-in-action?query=automated&utm_source=jin&utm_medium=affiliate&utm_campaign=affiliate&a_aid=jin"><img src="https://images.manning.com/360/480/resize/book/0/fc56aaf-b2ba-4ef4-85b3-4a31edbe8ecc/Song-AML-HI.png" alt="drawing" width="266"/></a>
 
 
<a href="https://www.manning.com/liveprojectseries/autokeras-ser"><img src="https://images.manning.com/360/480/resize/liveProjectSeries/9/38c715a-0c8c-4f66-b440-83d29993877a/ImageClassificationwithAutoKeras.jpg" alt="drawing" width="250"/></a>
</p>
## Installation
To install the package, please use the `pip` installation as follows:
```shell
pip3 install autokeras
```
Please follow the [installation guide](https://autokeras.com/install) for more details.
**Note:** Currently, AutoKeras is only compatible with **Python >= 3.7** and **TensorFlow >= 2.8.0**.
## Community
Ask your questions on our [GitHub Discussions](https://github.com/keras-team/autokeras/discussions).
## Contributing Code
Here is how we manage our project.
We pick the critical issues to work on from [GitHub issues](https://github.com/keras-team/autokeras/issues).
They will be added to this [Project](https://github.com/keras-team/autokeras/projects/3).
Some of the issues will then be added to the [milestones](https://github.com/keras-team/autokeras/milestones),
which are used to plan for the releases.
Refer to our [Contributing Guide](https://autokeras.com/contributing/) to learn the best practices.
Thank all the contributors!
[](https://github.com/keras-team/autokeras/graphs/contributor)
## Cite this work
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. "AutoKeras: An AutoML Library for Deep Learning." *the Journal of machine Learning research* 6 (2023): 1-6. ([Download](http://jmlr.org/papers/v24/20-1355.html))
Biblatex entry:
```bibtex
@article{JMLR:v24:20-1355,
author = {Haifeng Jin and François Chollet and Qingquan Song and Xia Hu},
title = {AutoKeras: An AutoML Library for Deep Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {6},
pages = {1--6},
url = {http://jmlr.org/papers/v24/20-1355.html}
}
```
## Acknowledgements
The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.
%prep
%autosetup -n autokeras-1.1.0
%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-autokeras -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated
|
