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@@ -0,0 +1 @@ +/Lasagne-0.1.tar.gz diff --git a/python-lasagne.spec b/python-lasagne.spec new file mode 100644 index 0000000..3fcaaf5 --- /dev/null +++ b/python-lasagne.spec @@ -0,0 +1,135 @@ +%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 +* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1-1 +- Package Spec generated @@ -0,0 +1 @@ +44212b92bf5f3b1be3021fa0b64b5fdb Lasagne-0.1.tar.gz |