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        "author": "Marcnuth",
        "author_email": "hxianxian@gmail.com",
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            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Libraries"
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        "description": "# Keras Models Hub\n\n![PyPI - Downloads](https://img.shields.io/pypi/dm/keras-models?label=PyPI)\n\nThis repo aims at providing both **reusable** Keras Models and **pre-trained** models, which could easily integrated into your projects.\n\n## Install\n\n```shell\npip install keras-models\n```\n\nIf you will using the NLP models, you need run one more command:\n```shell\npython -m spacy download xx_ent_wiki_sm\n```\n\n## Usage Guide\n\n### Import\n\n```\nimport kearasmodels\n```\n\n\n### Examples\n\n#### Reusable Models\n\n__LinearModel__\n\n__DNN__\n\n__CNN__\n\n```python\nfrom keras_models.models import CNN\n\n# fake data\nX = np.random.normal(0, 1.0, size=500 * 100 * 100 * 3).reshape(500, 100, 100, 3)\nw1 = np.random.normal(0, 1.0, size=100)\nw2 = np.random.normal(0, 1.0, size=3)\nY = np.dot(np.dot(np.dot(X, w2), w1), w1) + np.random.randint(1)\n\n# initialize & train model\nmodel = 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)\nmodel.compile(optimizer='adam', loss=mean_squared_error, metrics=['mae', 'mse'])\nmodel.summary()\n\nmodel.fit(X, Y, batch_size=16, epochs=100, validation_split=0.1)\n```\n\n__SkipGram__\n\n__WideDeep__\n\n#### Pre-trained Models\n\n__VGG16_Places365__\n> This model is forked from [GKalliatakis/Keras-VGG16-places365](https://github.com/GKalliatakis/Keras-VGG16-places365) and [CSAILVision/places365](https://github.com/CSAILVision/places365)\n\n```python\nfrom keras_models.models.pretrained import vgg16_places365\nlabels = vgg16_places365.predict(['your_image_file_pathname.jpg', 'another.jpg'], n_top=3)\n\n# Example Result: labels = [['cafeteria', 'food_court', 'restaurant_patio'], ['beach', 'sand']]\n```\n\n\n## Models\n\n- LinearModel\n- DNN\n- WideDeep\n- TextCNN\n- TextDNN\n- SkipGram\n- ResNet\n- VGG16_Places365 [pre-trained]\n- working on more models\n\n## Citation\n\n__WideDeep__\n\n```\nCheng H T, Koc L, Harmsen J, et al. \nWide & deep learning for recommender systems[C]\nProceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10.\n```\n\n__TextCNN__\n\n```\nKim Y. \nConvolutional neural networks for sentence classification[J]. \narXiv preprint arXiv:1408.5882, 2014.\n```\n\n__SkipGram__\n\n```\nMikolov T, Chen K, Corrado G, et al. \nEfficient estimation of word representations in vector space[J]. \narXiv preprint arXiv:1301.3781, 2013.\n```\n\n\n__VGG16_Places365__\n```\nZhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A.\nPlaces: A 10 million Image Database for Scene Recognition\nIEEE Transactions on Pattern Analysis and Machine Intelligence\n```\n\n__ResNet__\n```\nHe K, Zhang X, Ren S, et al. \nDeep residual learning for image recognition[C]\nProceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.\n\n```\n\n## Contribution\n\nPlease submit PR if you want to contribute, or submit issues for new model requirements.\n\n\n\n",
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