Metadata-Version: 2.1
Name: keras-models
Version: 0.0.7
Summary: Keras Models Hub
Home-page: https://github.com/Marcnuth/Keras-Models
Author: Marcnuth
Author-email: hxianxian@gmail.com
License: Apache License 2.0
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.
        
        
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
