automatic import of python-keras-self-attention

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+/keras-self-attention-0.51.0.tar.gz
diff --git a/python-keras-self-attention.spec b/python-keras-self-attention.spec
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+%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
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.51.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6eadd2d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7bc0e7a51eb634705a34b7a7361261d5  keras-self-attention-0.51.0.tar.gz