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diff --git a/python-keras-pos-embd.spec b/python-keras-pos-embd.spec new file mode 100644 index 0000000..eb5af27 --- /dev/null +++ b/python-keras-pos-embd.spec @@ -0,0 +1,351 @@ +%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 + +[](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 + +[](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 + +[](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 |