%global _empty_manifest_terminate_build 0
Name:		python-keras-pos-embd
Version:	0.13.0
Release:	1
Summary:	Position embedding layers in Keras
License:	MIT
URL:		https://github.com/CyberZHG/keras-pos-embd
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a3/f0/8803f9ac4cddd9d2640347bde2f451d7d33bbc99d761f2bc00fb15911bf6/keras-pos-embd-0.13.0.tar.gz
BuildArch:	noarch


%description
# Keras Position Embedding

[![Version](https://img.shields.io/pypi/v/keras-pos-embd.svg)](https://pypi.org/project/keras-pos-embd/)

\[[中文](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.md)\]

Position embedding layers in Keras.

## Install

```bash
pip install keras-pos-embd
```

## Usage

### Trainable Embedding

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(PositionEmbedding(
    input_shape=(None,),
    input_dim=10,     # The maximum absolute value of positions.
    output_dim=2,     # The dimension of embeddings.
    mask_zero=10000,  # The index that presents padding (because `0` will be used in relative positioning).
    mode=PositionEmbedding.MODE_EXPAND,
))
model.compile('adam', 'mse')
model.summary()
```

Note that you don't need to enable `mask_zero` if you want to add/concatenate other layers like word embeddings with masks:

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(PositionEmbedding(
    input_dim=100,
    output_dim=5,
    mode=PositionEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

### Sin & Cos Embedding

The [sine and cosine embedding](https://arxiv.org/pdf/1706.03762) has no trainable weights. The layer has three modes, it works just like `PositionEmbedding` in `expand` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(TrigPosEmbedding(
    input_shape=(None,),
    output_dim=30,                      # The dimension of embeddings.
    mode=TrigPosEmbedding.MODE_EXPAND,  # Use `expand` mode
))
model.compile('adam', 'mse')
model.summary()
```

If you want to add this embedding to existed embedding, then there is no need to add a position input in `add` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(TrigPosEmbedding(
    output_dim=5,
    mode=TrigPosEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

%package -n python3-keras-pos-embd
Summary:	Position embedding layers in Keras
Provides:	python-keras-pos-embd
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-keras-pos-embd
# Keras Position Embedding

[![Version](https://img.shields.io/pypi/v/keras-pos-embd.svg)](https://pypi.org/project/keras-pos-embd/)

\[[中文](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.md)\]

Position embedding layers in Keras.

## Install

```bash
pip install keras-pos-embd
```

## Usage

### Trainable Embedding

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(PositionEmbedding(
    input_shape=(None,),
    input_dim=10,     # The maximum absolute value of positions.
    output_dim=2,     # The dimension of embeddings.
    mask_zero=10000,  # The index that presents padding (because `0` will be used in relative positioning).
    mode=PositionEmbedding.MODE_EXPAND,
))
model.compile('adam', 'mse')
model.summary()
```

Note that you don't need to enable `mask_zero` if you want to add/concatenate other layers like word embeddings with masks:

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(PositionEmbedding(
    input_dim=100,
    output_dim=5,
    mode=PositionEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

### Sin & Cos Embedding

The [sine and cosine embedding](https://arxiv.org/pdf/1706.03762) has no trainable weights. The layer has three modes, it works just like `PositionEmbedding` in `expand` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(TrigPosEmbedding(
    input_shape=(None,),
    output_dim=30,                      # The dimension of embeddings.
    mode=TrigPosEmbedding.MODE_EXPAND,  # Use `expand` mode
))
model.compile('adam', 'mse')
model.summary()
```

If you want to add this embedding to existed embedding, then there is no need to add a position input in `add` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(TrigPosEmbedding(
    output_dim=5,
    mode=TrigPosEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

%package help
Summary:	Development documents and examples for keras-pos-embd
Provides:	python3-keras-pos-embd-doc
%description help
# Keras Position Embedding

[![Version](https://img.shields.io/pypi/v/keras-pos-embd.svg)](https://pypi.org/project/keras-pos-embd/)

\[[中文](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-pos-embd/blob/master/README.md)\]

Position embedding layers in Keras.

## Install

```bash
pip install keras-pos-embd
```

## Usage

### Trainable Embedding

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(PositionEmbedding(
    input_shape=(None,),
    input_dim=10,     # The maximum absolute value of positions.
    output_dim=2,     # The dimension of embeddings.
    mask_zero=10000,  # The index that presents padding (because `0` will be used in relative positioning).
    mode=PositionEmbedding.MODE_EXPAND,
))
model.compile('adam', 'mse')
model.summary()
```

Note that you don't need to enable `mask_zero` if you want to add/concatenate other layers like word embeddings with masks:

```python
from tensorflow import keras
from keras_pos_embd import PositionEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(PositionEmbedding(
    input_dim=100,
    output_dim=5,
    mode=PositionEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

### Sin & Cos Embedding

The [sine and cosine embedding](https://arxiv.org/pdf/1706.03762) has no trainable weights. The layer has three modes, it works just like `PositionEmbedding` in `expand` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(TrigPosEmbedding(
    input_shape=(None,),
    output_dim=30,                      # The dimension of embeddings.
    mode=TrigPosEmbedding.MODE_EXPAND,  # Use `expand` mode
))
model.compile('adam', 'mse')
model.summary()
```

If you want to add this embedding to existed embedding, then there is no need to add a position input in `add` mode:

```python
from tensorflow import keras
from keras_pos_embd import TrigPosEmbedding

model = keras.models.Sequential()
model.add(keras.layers.Embedding(
    input_shape=(None,),
    input_dim=10,
    output_dim=5,
    mask_zero=True,
))
model.add(TrigPosEmbedding(
    output_dim=5,
    mode=TrigPosEmbedding.MODE_ADD,
))
model.compile('adam', 'mse')
model.summary()
```

%prep
%autosetup -n keras-pos-embd-0.13.0

%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-pos-embd -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.13.0-1
- Package Spec generated