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%global _empty_manifest_terminate_build 0
Name: python-keras-embed-sim
Version: 0.10.0
Release: 1
Summary: Calculate similarity with embedding
License: MIT
URL: https://github.com/CyberZHG/keras-embed-sim
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d9/ac/641978394aaa28d90a81ae3e999ed8206c4c3223b7a668a5aa9ed6034db0/keras-embed-sim-0.10.0.tar.gz
BuildArch: noarch
%description
# Keras Embedding Similarity
[](https://pypi.org/project/keras-embed-sim/)
\[[中文](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.md)\]
Compute the similarity between the outputs and the embeddings.
## Install
```bash
pip install keras-embed-sim
```
## Usage
```python
from tensorflow import keras
from keras_embed_sim import EmbeddingRet, EmbeddingSim
input_layer = keras.layers.Input(shape=(None,), name='Input')
embed, embed_weights = EmbeddingRet(
input_dim=20,
output_dim=100,
mask_zero=True,
)(input_layer)
output_layer = EmbeddingSim()([embed, embed_weights])
```
%package -n python3-keras-embed-sim
Summary: Calculate similarity with embedding
Provides: python-keras-embed-sim
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-keras-embed-sim
# Keras Embedding Similarity
[](https://pypi.org/project/keras-embed-sim/)
\[[中文](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.md)\]
Compute the similarity between the outputs and the embeddings.
## Install
```bash
pip install keras-embed-sim
```
## Usage
```python
from tensorflow import keras
from keras_embed_sim import EmbeddingRet, EmbeddingSim
input_layer = keras.layers.Input(shape=(None,), name='Input')
embed, embed_weights = EmbeddingRet(
input_dim=20,
output_dim=100,
mask_zero=True,
)(input_layer)
output_layer = EmbeddingSim()([embed, embed_weights])
```
%package help
Summary: Development documents and examples for keras-embed-sim
Provides: python3-keras-embed-sim-doc
%description help
# Keras Embedding Similarity
[](https://pypi.org/project/keras-embed-sim/)
\[[中文](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-embed-sim/blob/master/README.md)\]
Compute the similarity between the outputs and the embeddings.
## Install
```bash
pip install keras-embed-sim
```
## Usage
```python
from tensorflow import keras
from keras_embed_sim import EmbeddingRet, EmbeddingSim
input_layer = keras.layers.Input(shape=(None,), name='Input')
embed, embed_weights = EmbeddingRet(
input_dim=20,
output_dim=100,
mask_zero=True,
)(input_layer)
output_layer = EmbeddingSim()([embed, embed_weights])
```
%prep
%autosetup -n keras-embed-sim-0.10.0
%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-keras-embed-sim -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.10.0-1
- Package Spec generated
|
