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%global _empty_manifest_terminate_build 0
Name: python-Lasagne
Version: 0.1
Release: 1
Summary: A lightweight library to build and train neural networks in Theano
License: MIT
URL: https://github.com/Lasagne/Lasagne
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/98/bf/4b2336e4dbc8c8859c4dd81b1cff18eef2066b4973a1bd2b0ca2e5435f35/Lasagne-0.1.tar.gz
BuildArch: noarch
%description
Lasagne is a lightweight library to build and train neural networks in Theano.
Its main features are:
* Supports feed-forward networks such as Convolutional Neural Networks (CNNs),
recurrent networks including Long Short-Term Memory (LSTM), and any
combination thereof
* Allows architectures of multiple inputs and multiple outputs, including
auxiliary classifiers
* Many optimization methods including Nesterov momentum, RMSprop and ADAM
* Freely definable cost function and no need to derive gradients due to
Theano's symbolic differentiation
* Transparent support of CPUs and GPUs due to Theano's expression compiler
Its design is governed by `six principles
<http://lasagne.readthedocs.org/en/latest/user/development.html#philosophy>`_:
* Simplicity: Be easy to use, easy to understand and easy to extend, to
facilitate use in research
* Transparency: Do not hide Theano behind abstractions, directly process and
return Theano expressions or Python / numpy data types
* Modularity: Allow all parts (layers, regularizers, optimizers, ...) to be
used independently of Lasagne
* Pragmatism: Make common use cases easy, do not overrate uncommon cases
* Restraint: Do not obstruct users with features they decide not to use
* Focus: "Do one thing and do it well"
%package -n python3-Lasagne
Summary: A lightweight library to build and train neural networks in Theano
Provides: python-Lasagne
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Lasagne
Lasagne is a lightweight library to build and train neural networks in Theano.
Its main features are:
* Supports feed-forward networks such as Convolutional Neural Networks (CNNs),
recurrent networks including Long Short-Term Memory (LSTM), and any
combination thereof
* Allows architectures of multiple inputs and multiple outputs, including
auxiliary classifiers
* Many optimization methods including Nesterov momentum, RMSprop and ADAM
* Freely definable cost function and no need to derive gradients due to
Theano's symbolic differentiation
* Transparent support of CPUs and GPUs due to Theano's expression compiler
Its design is governed by `six principles
<http://lasagne.readthedocs.org/en/latest/user/development.html#philosophy>`_:
* Simplicity: Be easy to use, easy to understand and easy to extend, to
facilitate use in research
* Transparency: Do not hide Theano behind abstractions, directly process and
return Theano expressions or Python / numpy data types
* Modularity: Allow all parts (layers, regularizers, optimizers, ...) to be
used independently of Lasagne
* Pragmatism: Make common use cases easy, do not overrate uncommon cases
* Restraint: Do not obstruct users with features they decide not to use
* Focus: "Do one thing and do it well"
%package help
Summary: Development documents and examples for Lasagne
Provides: python3-Lasagne-doc
%description help
Lasagne is a lightweight library to build and train neural networks in Theano.
Its main features are:
* Supports feed-forward networks such as Convolutional Neural Networks (CNNs),
recurrent networks including Long Short-Term Memory (LSTM), and any
combination thereof
* Allows architectures of multiple inputs and multiple outputs, including
auxiliary classifiers
* Many optimization methods including Nesterov momentum, RMSprop and ADAM
* Freely definable cost function and no need to derive gradients due to
Theano's symbolic differentiation
* Transparent support of CPUs and GPUs due to Theano's expression compiler
Its design is governed by `six principles
<http://lasagne.readthedocs.org/en/latest/user/development.html#philosophy>`_:
* Simplicity: Be easy to use, easy to understand and easy to extend, to
facilitate use in research
* Transparency: Do not hide Theano behind abstractions, directly process and
return Theano expressions or Python / numpy data types
* Modularity: Allow all parts (layers, regularizers, optimizers, ...) to be
used independently of Lasagne
* Pragmatism: Make common use cases easy, do not overrate uncommon cases
* Restraint: Do not obstruct users with features they decide not to use
* Focus: "Do one thing and do it well"
%prep
%autosetup -n Lasagne-0.1
%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-Lasagne -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1-1
- Package Spec generated
|
