%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 `_: * 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 `_: * 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 `_: * 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 07 2023 Python_Bot - 0.1-1 - Package Spec generated