%global _empty_manifest_terminate_build 0
Name:		python-keras-multi-head
Version:	0.29.0
Release:	1
Summary:	A wrapper layer for stacking layers horizontally
License:	MIT
URL:		https://github.com/CyberZHG/keras-multi-head
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2c/21/5e1699e9d63a8e3c0d5fd0716b9a8be7d8c2c07fc8de34902e55de5ba58e/keras-multi-head-0.29.0.tar.gz
BuildArch:	noarch


%description
# Keras Multi-Head

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

A wrapper layer for stacking layers horizontally.

![](https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png)

## Install

```bash
pip install keras-multi-head
```

## Usage

### Duplicate Layers

The layer will be duplicated if only a single layer is provided. The `layer_num` argument controls how many layers will be duplicated eventually.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead(keras.layers.LSTM(units=32), layer_num=5, name='Multi-LSTMs'))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=4, activation='softmax', name='Dense'))
model.build()
model.summary()
```

### Use Multiple-Layers

The first argument could also be a list of layers with different configurations, however, they must have the same output shapes.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead([
    keras.layers.Conv1D(filters=32, kernel_size=3, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=5, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=7, padding='same'),
], name='Multi-CNNs'))
model.build()
model.summary()
```

### Linear Transformation

The input data will be mapped to different values of the same shape for each layer when `hidden_dim` is given.

### Regularization

![](https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png)

The regularization is used when you expect to extract different features from the parallel layers. You can customize the indices of weights in the layers, the intervals represent the parts of the weights and the factor of the regularization.

For example, the bidirectional LSTM layer has 6 weights by default, and the first 3s belong to the forward layer. The 2nd weight (recurrent kernel) in the forward layer controls the computation of gates for recurrent connections. The kernel for computing cell states lays in units x 2 to units x 3 of the recurrent kernel. We can used the regularization for the kernels:

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=5, output_dim=3, name='Embed'))
model.add(MultiHead(
    layer=keras.layers.Bidirectional(keras.layers.LSTM(units=16), name='LSTM'),
    layer_num=5,
    reg_index=[1, 4],
    reg_slice=(slice(None, None), slice(32, 48)),
    reg_factor=0.1,
    name='Multi-Head-Attention',
))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=2, activation='softmax', name='Dense'))
model.build()
```

* `reg_index`: The indices of `layer.get_weights()`, a single integer or a list of integers.
* `reg_slice`: `slice`s or a tuple of `slice`s or a list of the previous choices. If multiple indices are provided in `reg_index` and `reg_slice` is not a list, then `reg_slice` is assumed to be equal for all the indices. The whole array will be used if you leave this argument to `None`.
* `reg_factor`: The factor of the regularization, a float or a list of floats.

### Multi-Head Attention

A more specific multi-head layer is provided (since the general one is harder to use). The layer uses scaled dot product attention layers as its sub-layers and only `head_num` is required:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_layer = keras.layers.Input(
    shape=(2, 3),
    name='Input',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

The shapes of input and output tensors would be the same if only one layer is presented as input. The input layers will be considered as query, key and value when a list is given:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_query = keras.layers.Input(
    shape=(2, 3),
    name='Input-Q',
)
input_key = keras.layers.Input(
    shape=(4, 5),
    name='Input-K',
)
input_value = keras.layers.Input(
    shape=(4, 6),
    name='Input-V',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)([input_query, input_key, input_value])
model = keras.models.Model(inputs=[input_query, input_key, input_value], outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

%package -n python3-keras-multi-head
Summary:	A wrapper layer for stacking layers horizontally
Provides:	python-keras-multi-head
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-keras-multi-head
# Keras Multi-Head

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

A wrapper layer for stacking layers horizontally.

![](https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png)

## Install

```bash
pip install keras-multi-head
```

## Usage

### Duplicate Layers

The layer will be duplicated if only a single layer is provided. The `layer_num` argument controls how many layers will be duplicated eventually.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead(keras.layers.LSTM(units=32), layer_num=5, name='Multi-LSTMs'))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=4, activation='softmax', name='Dense'))
model.build()
model.summary()
```

### Use Multiple-Layers

The first argument could also be a list of layers with different configurations, however, they must have the same output shapes.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead([
    keras.layers.Conv1D(filters=32, kernel_size=3, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=5, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=7, padding='same'),
], name='Multi-CNNs'))
model.build()
model.summary()
```

### Linear Transformation

The input data will be mapped to different values of the same shape for each layer when `hidden_dim` is given.

### Regularization

![](https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png)

The regularization is used when you expect to extract different features from the parallel layers. You can customize the indices of weights in the layers, the intervals represent the parts of the weights and the factor of the regularization.

For example, the bidirectional LSTM layer has 6 weights by default, and the first 3s belong to the forward layer. The 2nd weight (recurrent kernel) in the forward layer controls the computation of gates for recurrent connections. The kernel for computing cell states lays in units x 2 to units x 3 of the recurrent kernel. We can used the regularization for the kernels:

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=5, output_dim=3, name='Embed'))
model.add(MultiHead(
    layer=keras.layers.Bidirectional(keras.layers.LSTM(units=16), name='LSTM'),
    layer_num=5,
    reg_index=[1, 4],
    reg_slice=(slice(None, None), slice(32, 48)),
    reg_factor=0.1,
    name='Multi-Head-Attention',
))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=2, activation='softmax', name='Dense'))
model.build()
```

* `reg_index`: The indices of `layer.get_weights()`, a single integer or a list of integers.
* `reg_slice`: `slice`s or a tuple of `slice`s or a list of the previous choices. If multiple indices are provided in `reg_index` and `reg_slice` is not a list, then `reg_slice` is assumed to be equal for all the indices. The whole array will be used if you leave this argument to `None`.
* `reg_factor`: The factor of the regularization, a float or a list of floats.

### Multi-Head Attention

A more specific multi-head layer is provided (since the general one is harder to use). The layer uses scaled dot product attention layers as its sub-layers and only `head_num` is required:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_layer = keras.layers.Input(
    shape=(2, 3),
    name='Input',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

The shapes of input and output tensors would be the same if only one layer is presented as input. The input layers will be considered as query, key and value when a list is given:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_query = keras.layers.Input(
    shape=(2, 3),
    name='Input-Q',
)
input_key = keras.layers.Input(
    shape=(4, 5),
    name='Input-K',
)
input_value = keras.layers.Input(
    shape=(4, 6),
    name='Input-V',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)([input_query, input_key, input_value])
model = keras.models.Model(inputs=[input_query, input_key, input_value], outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

%package help
Summary:	Development documents and examples for keras-multi-head
Provides:	python3-keras-multi-head-doc
%description help
# Keras Multi-Head

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

A wrapper layer for stacking layers horizontally.

![](https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png)

## Install

```bash
pip install keras-multi-head
```

## Usage

### Duplicate Layers

The layer will be duplicated if only a single layer is provided. The `layer_num` argument controls how many layers will be duplicated eventually.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead(keras.layers.LSTM(units=32), layer_num=5, name='Multi-LSTMs'))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=4, activation='softmax', name='Dense'))
model.build()
model.summary()
```

### Use Multiple-Layers

The first argument could also be a list of layers with different configurations, however, they must have the same output shapes.

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
model.add(MultiHead([
    keras.layers.Conv1D(filters=32, kernel_size=3, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=5, padding='same'),
    keras.layers.Conv1D(filters=32, kernel_size=7, padding='same'),
], name='Multi-CNNs'))
model.build()
model.summary()
```

### Linear Transformation

The input data will be mapped to different values of the same shape for each layer when `hidden_dim` is given.

### Regularization

![](https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png)

The regularization is used when you expect to extract different features from the parallel layers. You can customize the indices of weights in the layers, the intervals represent the parts of the weights and the factor of the regularization.

For example, the bidirectional LSTM layer has 6 weights by default, and the first 3s belong to the forward layer. The 2nd weight (recurrent kernel) in the forward layer controls the computation of gates for recurrent connections. The kernel for computing cell states lays in units x 2 to units x 3 of the recurrent kernel. We can used the regularization for the kernels:

```python
from tensorflow import keras
from keras_multi_head import MultiHead


model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=5, output_dim=3, name='Embed'))
model.add(MultiHead(
    layer=keras.layers.Bidirectional(keras.layers.LSTM(units=16), name='LSTM'),
    layer_num=5,
    reg_index=[1, 4],
    reg_slice=(slice(None, None), slice(32, 48)),
    reg_factor=0.1,
    name='Multi-Head-Attention',
))
model.add(keras.layers.Flatten(name='Flatten'))
model.add(keras.layers.Dense(units=2, activation='softmax', name='Dense'))
model.build()
```

* `reg_index`: The indices of `layer.get_weights()`, a single integer or a list of integers.
* `reg_slice`: `slice`s or a tuple of `slice`s or a list of the previous choices. If multiple indices are provided in `reg_index` and `reg_slice` is not a list, then `reg_slice` is assumed to be equal for all the indices. The whole array will be used if you leave this argument to `None`.
* `reg_factor`: The factor of the regularization, a float or a list of floats.

### Multi-Head Attention

A more specific multi-head layer is provided (since the general one is harder to use). The layer uses scaled dot product attention layers as its sub-layers and only `head_num` is required:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_layer = keras.layers.Input(
    shape=(2, 3),
    name='Input',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

The shapes of input and output tensors would be the same if only one layer is presented as input. The input layers will be considered as query, key and value when a list is given:

```python
from tensorflow import keras
from keras_multi_head import MultiHeadAttention

input_query = keras.layers.Input(
    shape=(2, 3),
    name='Input-Q',
)
input_key = keras.layers.Input(
    shape=(4, 5),
    name='Input-K',
)
input_value = keras.layers.Input(
    shape=(4, 6),
    name='Input-V',
)
att_layer = MultiHeadAttention(
    head_num=3,
    name='Multi-Head',
)([input_query, input_key, input_value])
model = keras.models.Model(inputs=[input_query, input_key, input_value], outputs=att_layer)
model.compile(
    optimizer='adam',
    loss='mse',
    metrics={},
)
model.summary()
```

%prep
%autosetup -n keras-multi-head-0.29.0

%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-multi-head -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.29.0-1
- Package Spec generated