%global _empty_manifest_terminate_build 0
Name:		python-keras-self-attention
Version:	0.51.0
Release:	1
Summary:	Attention mechanism for processing sequential data that considers the context for each timestamp
License:	MIT
URL:		https://github.com/CyberZHG/keras-self-attention
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d5/a5/0a1d003e420da49791f64def11d8d2837280e1a680c2eaaab216f9f17ed7/keras-self-attention-0.51.0.tar.gz
BuildArch:	noarch


%description
# Keras Self-Attention

[![Version](https://img.shields.io/pypi/v/keras-self-attention.svg)](https://pypi.org/project/keras-self-attention/)
![License](https://img.shields.io/pypi/l/keras-self-attention.svg)

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

Attention mechanism for processing sequential data that considers the context for each timestamp.

* ![](https://user-images.githubusercontent.com/853842/44248592-1fbd0500-a21e-11e8-9fe0-52a1e4a48329.gif)
* ![](https://user-images.githubusercontent.com/853842/44248591-1e8bd800-a21e-11e8-9ca8-9198c2725108.gif)
* ![](https://user-images.githubusercontent.com/853842/44248590-1df34180-a21e-11e8-8ff1-268217f466ba.gif)
* ![](https://user-images.githubusercontent.com/853842/44249018-8ba06d00-a220-11e8-80e3-802677b658ed.gif)

## Install

```bash
pip install keras-self-attention
```

## Usage

### Basic

By default, the attention layer uses additive attention and considers the whole context while calculating the relevance. The following code creates an attention layer that follows the equations in the first section (`attention_activation` is the activation function of `e_{t, t'}`):

```python
import keras
from keras_self_attention import SeqSelfAttention


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=10000,
                                 output_dim=300,
                                 mask_zero=True))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128,
                                                       return_sequences=True)))
model.add(SeqSelfAttention(attention_activation='sigmoid'))
model.add(keras.layers.Dense(units=5))
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['categorical_accuracy'],
)
model.summary()
```

### Local Attention

The global context may be too broad for one piece of data. The parameter `attention_width` controls the width of the local context:

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_activation='sigmoid',
    name='Attention',
)
```

### Multiplicative Attention

You can use multiplicative attention by setting `attention_type`:

![](https://user-images.githubusercontent.com/853842/44253887-a03a3080-a233-11e8-9d49-3fd7e622a0f7.gif)

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
    attention_activation=None,
    kernel_regularizer=keras.regularizers.l2(1e-6),
    use_attention_bias=False,
    name='Attention',
)
```

### Regularizer

![](https://user-images.githubusercontent.com/853842/44250188-f99b6300-a225-11e8-8fab-8dcf0d99616e.gif)

To use the regularizer, set `attention_regularizer_weight` to a positive number:

```python
import keras
from keras_self_attention import SeqSelfAttention

inputs = keras.layers.Input(shape=(None,))
embd = keras.layers.Embedding(input_dim=32,
                              output_dim=16,
                              mask_zero=True)(inputs)
lstm = keras.layers.Bidirectional(keras.layers.LSTM(units=16,
                                                    return_sequences=True))(embd)
att = SeqSelfAttention(attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
                       kernel_regularizer=keras.regularizers.l2(1e-4),
                       bias_regularizer=keras.regularizers.l1(1e-4),
                       attention_regularizer_weight=1e-4,
                       name='Attention')(lstm)
dense = keras.layers.Dense(units=5, name='Dense')(att)
model = keras.models.Model(inputs=inputs, outputs=[dense])
model.compile(
    optimizer='adam',
    loss={'Dense': 'sparse_categorical_crossentropy'},
    metrics={'Dense': 'categorical_accuracy'},
)
model.summary(line_length=100)
```

### Load the Model

Make sure to add `SeqSelfAttention` to custom objects:

```python
import keras

keras.models.load_model(model_path, custom_objects=SeqSelfAttention.get_custom_objects())
```

### History Only

Set `history_only` to `True` when only historical data could be used:

```python
SeqSelfAttention(
    attention_width=3,
    history_only=True,
    name='Attention',
)
```

### Multi-Head

Please refer to [keras-multi-head](https://github.com/CyberZHG/keras-multi-head).

%package -n python3-keras-self-attention
Summary:	Attention mechanism for processing sequential data that considers the context for each timestamp
Provides:	python-keras-self-attention
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-keras-self-attention
# Keras Self-Attention

[![Version](https://img.shields.io/pypi/v/keras-self-attention.svg)](https://pypi.org/project/keras-self-attention/)
![License](https://img.shields.io/pypi/l/keras-self-attention.svg)

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

Attention mechanism for processing sequential data that considers the context for each timestamp.

* ![](https://user-images.githubusercontent.com/853842/44248592-1fbd0500-a21e-11e8-9fe0-52a1e4a48329.gif)
* ![](https://user-images.githubusercontent.com/853842/44248591-1e8bd800-a21e-11e8-9ca8-9198c2725108.gif)
* ![](https://user-images.githubusercontent.com/853842/44248590-1df34180-a21e-11e8-8ff1-268217f466ba.gif)
* ![](https://user-images.githubusercontent.com/853842/44249018-8ba06d00-a220-11e8-80e3-802677b658ed.gif)

## Install

```bash
pip install keras-self-attention
```

## Usage

### Basic

By default, the attention layer uses additive attention and considers the whole context while calculating the relevance. The following code creates an attention layer that follows the equations in the first section (`attention_activation` is the activation function of `e_{t, t'}`):

```python
import keras
from keras_self_attention import SeqSelfAttention


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=10000,
                                 output_dim=300,
                                 mask_zero=True))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128,
                                                       return_sequences=True)))
model.add(SeqSelfAttention(attention_activation='sigmoid'))
model.add(keras.layers.Dense(units=5))
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['categorical_accuracy'],
)
model.summary()
```

### Local Attention

The global context may be too broad for one piece of data. The parameter `attention_width` controls the width of the local context:

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_activation='sigmoid',
    name='Attention',
)
```

### Multiplicative Attention

You can use multiplicative attention by setting `attention_type`:

![](https://user-images.githubusercontent.com/853842/44253887-a03a3080-a233-11e8-9d49-3fd7e622a0f7.gif)

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
    attention_activation=None,
    kernel_regularizer=keras.regularizers.l2(1e-6),
    use_attention_bias=False,
    name='Attention',
)
```

### Regularizer

![](https://user-images.githubusercontent.com/853842/44250188-f99b6300-a225-11e8-8fab-8dcf0d99616e.gif)

To use the regularizer, set `attention_regularizer_weight` to a positive number:

```python
import keras
from keras_self_attention import SeqSelfAttention

inputs = keras.layers.Input(shape=(None,))
embd = keras.layers.Embedding(input_dim=32,
                              output_dim=16,
                              mask_zero=True)(inputs)
lstm = keras.layers.Bidirectional(keras.layers.LSTM(units=16,
                                                    return_sequences=True))(embd)
att = SeqSelfAttention(attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
                       kernel_regularizer=keras.regularizers.l2(1e-4),
                       bias_regularizer=keras.regularizers.l1(1e-4),
                       attention_regularizer_weight=1e-4,
                       name='Attention')(lstm)
dense = keras.layers.Dense(units=5, name='Dense')(att)
model = keras.models.Model(inputs=inputs, outputs=[dense])
model.compile(
    optimizer='adam',
    loss={'Dense': 'sparse_categorical_crossentropy'},
    metrics={'Dense': 'categorical_accuracy'},
)
model.summary(line_length=100)
```

### Load the Model

Make sure to add `SeqSelfAttention` to custom objects:

```python
import keras

keras.models.load_model(model_path, custom_objects=SeqSelfAttention.get_custom_objects())
```

### History Only

Set `history_only` to `True` when only historical data could be used:

```python
SeqSelfAttention(
    attention_width=3,
    history_only=True,
    name='Attention',
)
```

### Multi-Head

Please refer to [keras-multi-head](https://github.com/CyberZHG/keras-multi-head).

%package help
Summary:	Development documents and examples for keras-self-attention
Provides:	python3-keras-self-attention-doc
%description help
# Keras Self-Attention

[![Version](https://img.shields.io/pypi/v/keras-self-attention.svg)](https://pypi.org/project/keras-self-attention/)
![License](https://img.shields.io/pypi/l/keras-self-attention.svg)

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

Attention mechanism for processing sequential data that considers the context for each timestamp.

* ![](https://user-images.githubusercontent.com/853842/44248592-1fbd0500-a21e-11e8-9fe0-52a1e4a48329.gif)
* ![](https://user-images.githubusercontent.com/853842/44248591-1e8bd800-a21e-11e8-9ca8-9198c2725108.gif)
* ![](https://user-images.githubusercontent.com/853842/44248590-1df34180-a21e-11e8-8ff1-268217f466ba.gif)
* ![](https://user-images.githubusercontent.com/853842/44249018-8ba06d00-a220-11e8-80e3-802677b658ed.gif)

## Install

```bash
pip install keras-self-attention
```

## Usage

### Basic

By default, the attention layer uses additive attention and considers the whole context while calculating the relevance. The following code creates an attention layer that follows the equations in the first section (`attention_activation` is the activation function of `e_{t, t'}`):

```python
import keras
from keras_self_attention import SeqSelfAttention


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=10000,
                                 output_dim=300,
                                 mask_zero=True))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128,
                                                       return_sequences=True)))
model.add(SeqSelfAttention(attention_activation='sigmoid'))
model.add(keras.layers.Dense(units=5))
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['categorical_accuracy'],
)
model.summary()
```

### Local Attention

The global context may be too broad for one piece of data. The parameter `attention_width` controls the width of the local context:

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_activation='sigmoid',
    name='Attention',
)
```

### Multiplicative Attention

You can use multiplicative attention by setting `attention_type`:

![](https://user-images.githubusercontent.com/853842/44253887-a03a3080-a233-11e8-9d49-3fd7e622a0f7.gif)

```python
from keras_self_attention import SeqSelfAttention

SeqSelfAttention(
    attention_width=15,
    attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
    attention_activation=None,
    kernel_regularizer=keras.regularizers.l2(1e-6),
    use_attention_bias=False,
    name='Attention',
)
```

### Regularizer

![](https://user-images.githubusercontent.com/853842/44250188-f99b6300-a225-11e8-8fab-8dcf0d99616e.gif)

To use the regularizer, set `attention_regularizer_weight` to a positive number:

```python
import keras
from keras_self_attention import SeqSelfAttention

inputs = keras.layers.Input(shape=(None,))
embd = keras.layers.Embedding(input_dim=32,
                              output_dim=16,
                              mask_zero=True)(inputs)
lstm = keras.layers.Bidirectional(keras.layers.LSTM(units=16,
                                                    return_sequences=True))(embd)
att = SeqSelfAttention(attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
                       kernel_regularizer=keras.regularizers.l2(1e-4),
                       bias_regularizer=keras.regularizers.l1(1e-4),
                       attention_regularizer_weight=1e-4,
                       name='Attention')(lstm)
dense = keras.layers.Dense(units=5, name='Dense')(att)
model = keras.models.Model(inputs=inputs, outputs=[dense])
model.compile(
    optimizer='adam',
    loss={'Dense': 'sparse_categorical_crossentropy'},
    metrics={'Dense': 'categorical_accuracy'},
)
model.summary(line_length=100)
```

### Load the Model

Make sure to add `SeqSelfAttention` to custom objects:

```python
import keras

keras.models.load_model(model_path, custom_objects=SeqSelfAttention.get_custom_objects())
```

### History Only

Set `history_only` to `True` when only historical data could be used:

```python
SeqSelfAttention(
    attention_width=3,
    history_only=True,
    name='Attention',
)
```

### Multi-Head

Please refer to [keras-multi-head](https://github.com/CyberZHG/keras-multi-head).

%prep
%autosetup -n keras-self-attention-0.51.0

%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-self-attention -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.51.0-1
- Package Spec generated