%global _empty_manifest_terminate_build 0 Name: python-keras-models Version: 0.0.7 Release: 1 Summary: Keras Models Hub License: Apache License 2.0 URL: https://github.com/Marcnuth/Keras-Models Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d4/a5/4d1dd4a1d31c56a28e32441404c01694faa13d384f7d679987eb16a0456e/keras-models-0.0.7.tar.gz BuildArch: noarch Requires: python3-keras Requires: python3-numpy Requires: python3-spacy Requires: python3-Pillow Requires: python3-opencv-python Requires: python3-pathlib %description # Keras Models Hub ![PyPI - Downloads](https://img.shields.io/pypi/dm/keras-models?label=PyPI) This repo aims at providing both **reusable** Keras Models and **pre-trained** models, which could easily integrated into your projects. ## Install ```shell pip install keras-models ``` If you will using the NLP models, you need run one more command: ```shell python -m spacy download xx_ent_wiki_sm ``` ## Usage Guide ### Import ``` import kearasmodels ``` ### Examples #### Reusable Models __LinearModel__ __DNN__ __CNN__ ```python from keras_models.models import CNN # fake data X = np.random.normal(0, 1.0, size=500 * 100 * 100 * 3).reshape(500, 100, 100, 3) w1 = np.random.normal(0, 1.0, size=100) w2 = np.random.normal(0, 1.0, size=3) Y = np.dot(np.dot(np.dot(X, w2), w1), w1) + np.random.randint(1) # initialize & train model model = CNN(input_shape=X.shape[1:], filters=[32, 64], kernel_size=(2, 2), pool_size=(3, 3), padding='same', r_dropout=0.25, num_classes=1) model.compile(optimizer='adam', loss=mean_squared_error, metrics=['mae', 'mse']) model.summary() model.fit(X, Y, batch_size=16, epochs=100, validation_split=0.1) ``` __SkipGram__ __WideDeep__ #### Pre-trained Models __VGG16_Places365__ > This model is forked from [GKalliatakis/Keras-VGG16-places365](https://github.com/GKalliatakis/Keras-VGG16-places365) and [CSAILVision/places365](https://github.com/CSAILVision/places365) ```python from keras_models.models.pretrained import vgg16_places365 labels = vgg16_places365.predict(['your_image_file_pathname.jpg', 'another.jpg'], n_top=3) # Example Result: labels = [['cafeteria', 'food_court', 'restaurant_patio'], ['beach', 'sand']] ``` ## Models - LinearModel - DNN - WideDeep - TextCNN - TextDNN - SkipGram - ResNet - VGG16_Places365 [pre-trained] - working on more models ## Citation __WideDeep__ ``` Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C] Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10. ``` __TextCNN__ ``` Kim Y. Convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1408.5882, 2014. ``` __SkipGram__ ``` Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. ``` __VGG16_Places365__ ``` Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. Places: A 10 million Image Database for Scene Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence ``` __ResNet__ ``` He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C] Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. ``` ## Contribution Please submit PR if you want to contribute, or submit issues for new model requirements. %package -n python3-keras-models Summary: Keras Models Hub Provides: python-keras-models BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-keras-models # Keras Models Hub ![PyPI - Downloads](https://img.shields.io/pypi/dm/keras-models?label=PyPI) This repo aims at providing both **reusable** Keras Models and **pre-trained** models, which could easily integrated into your projects. ## Install ```shell pip install keras-models ``` If you will using the NLP models, you need run one more command: ```shell python -m spacy download xx_ent_wiki_sm ``` ## Usage Guide ### Import ``` import kearasmodels ``` ### Examples #### Reusable Models __LinearModel__ __DNN__ __CNN__ ```python from keras_models.models import CNN # fake data X = np.random.normal(0, 1.0, size=500 * 100 * 100 * 3).reshape(500, 100, 100, 3) w1 = np.random.normal(0, 1.0, size=100) w2 = np.random.normal(0, 1.0, size=3) Y = np.dot(np.dot(np.dot(X, w2), w1), w1) + np.random.randint(1) # initialize & train model model = CNN(input_shape=X.shape[1:], filters=[32, 64], kernel_size=(2, 2), pool_size=(3, 3), padding='same', r_dropout=0.25, num_classes=1) model.compile(optimizer='adam', loss=mean_squared_error, metrics=['mae', 'mse']) model.summary() model.fit(X, Y, batch_size=16, epochs=100, validation_split=0.1) ``` __SkipGram__ __WideDeep__ #### Pre-trained Models __VGG16_Places365__ > This model is forked from [GKalliatakis/Keras-VGG16-places365](https://github.com/GKalliatakis/Keras-VGG16-places365) and [CSAILVision/places365](https://github.com/CSAILVision/places365) ```python from keras_models.models.pretrained import vgg16_places365 labels = vgg16_places365.predict(['your_image_file_pathname.jpg', 'another.jpg'], n_top=3) # Example Result: labels = [['cafeteria', 'food_court', 'restaurant_patio'], ['beach', 'sand']] ``` ## Models - LinearModel - DNN - WideDeep - TextCNN - TextDNN - SkipGram - ResNet - VGG16_Places365 [pre-trained] - working on more models ## Citation __WideDeep__ ``` Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C] Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10. ``` __TextCNN__ ``` Kim Y. Convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1408.5882, 2014. ``` __SkipGram__ ``` Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. ``` __VGG16_Places365__ ``` Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. Places: A 10 million Image Database for Scene Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence ``` __ResNet__ ``` He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C] Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. ``` ## Contribution Please submit PR if you want to contribute, or submit issues for new model requirements. %package help Summary: Development documents and examples for keras-models Provides: python3-keras-models-doc %description help # Keras Models Hub ![PyPI - Downloads](https://img.shields.io/pypi/dm/keras-models?label=PyPI) This repo aims at providing both **reusable** Keras Models and **pre-trained** models, which could easily integrated into your projects. ## Install ```shell pip install keras-models ``` If you will using the NLP models, you need run one more command: ```shell python -m spacy download xx_ent_wiki_sm ``` ## Usage Guide ### Import ``` import kearasmodels ``` ### Examples #### Reusable Models __LinearModel__ __DNN__ __CNN__ ```python from keras_models.models import CNN # fake data X = np.random.normal(0, 1.0, size=500 * 100 * 100 * 3).reshape(500, 100, 100, 3) w1 = np.random.normal(0, 1.0, size=100) w2 = np.random.normal(0, 1.0, size=3) Y = np.dot(np.dot(np.dot(X, w2), w1), w1) + np.random.randint(1) # initialize & train model model = CNN(input_shape=X.shape[1:], filters=[32, 64], kernel_size=(2, 2), pool_size=(3, 3), padding='same', r_dropout=0.25, num_classes=1) model.compile(optimizer='adam', loss=mean_squared_error, metrics=['mae', 'mse']) model.summary() model.fit(X, Y, batch_size=16, epochs=100, validation_split=0.1) ``` __SkipGram__ __WideDeep__ #### Pre-trained Models __VGG16_Places365__ > This model is forked from [GKalliatakis/Keras-VGG16-places365](https://github.com/GKalliatakis/Keras-VGG16-places365) and [CSAILVision/places365](https://github.com/CSAILVision/places365) ```python from keras_models.models.pretrained import vgg16_places365 labels = vgg16_places365.predict(['your_image_file_pathname.jpg', 'another.jpg'], n_top=3) # Example Result: labels = [['cafeteria', 'food_court', 'restaurant_patio'], ['beach', 'sand']] ``` ## Models - LinearModel - DNN - WideDeep - TextCNN - TextDNN - SkipGram - ResNet - VGG16_Places365 [pre-trained] - working on more models ## Citation __WideDeep__ ``` Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C] Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10. ``` __TextCNN__ ``` Kim Y. Convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1408.5882, 2014. ``` __SkipGram__ ``` Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. ``` __VGG16_Places365__ ``` Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. Places: A 10 million Image Database for Scene Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence ``` __ResNet__ ``` He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C] Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. ``` ## Contribution Please submit PR if you want to contribute, or submit issues for new model requirements. %prep %autosetup -n keras-models-0.0.7 %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-models -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 31 2023 Python_Bot - 0.0.7-1 - Package Spec generated