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    "info": {
        "author": "Cesar Perez",
        "author_email": "cperez@wnohang.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 1 - Planning",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Education",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "# zhang2016dependency\n\nThis package provides a simple implementation of the models proposed in\nthe paper:\n\n> Zhang, R., Lee, H., & Radev, D. (2016). Dependency sensitive convolutional neural networks for modeling sentences and documents. arXiv preprint arXiv:1611.02361.\n\n## Installation\nThis package depends on the [Keras](https://keras.io/) library. This\nmeans you will need to install a backend library in order to use this\nmodule. Take a look to [Keras installation](https://keras.io/#installation)\nto get more information.\n\nAfter having installed the backend of yout choice, you just need to\ninstall this package using [pip](https://pypi.org/):\n\n    pip install zhang2016dependency\n\n## Usage\nThis package only provides a single model. To get detailed information\non the parameters the model accepts, take a look to the documentation\nincluded with the module class.\n\nHere is a complete example of instantiation of the model proposed in the\noriginal paper using two channel of randomly initialized word\nembeddings:\n\n```python\nimport numpy as np\nimport numpy.random as rng\n\nvocabulary_size = 1000\nembedding_size = 300\n\nvalue = np.sqrt(6/embedding_size)\n\nweights_shape = (vocabulary_size+1, embedding_size)\nweights = rng.uniform(low=-value, high=value, size=weights_shape)\n\nchannels = [\n    {\n      'weights': [weights],\n      'trainable': False,\n      'input_dim': vocabulary_size + 1,\n      'output_dim': embedding_size,\n      'name': 'random-embedding-1'\n    },\n    {\n      'weights': [weights],\n      'trainable': True,\n      'input_dim': vocabulary_size + 1,\n      'output_dim': embedding_size,\n      'name': 'random-embedding-2'\n    }\n]\n\nwindows = [\n    {\n        'filters': 100,\n        'kernel_size': 3,\n        'activation': 'relu',\n        'name': '3-grams'\n    },\n    {\n        'filters': 100,\n        'kernel_size': 4,\n        'activation': 'relu',\n        'name': '4-grams'\n    },\n    {\n        'filters': 100,\n        'kernel_size': 5,\n        'activation': 'relu',\n        'name': '5-grams'\n    }\n]\n\nfrom zhang2016dependency import Model\n\nmodel = Model(channels=channels,\n              windows=windows,\n              sentence_length=37,\n              num_classes=6,\n              dropout_rate=0.5,\n              classifier_activation='softmax',\n              include_top=True,\n              name='DSCNN')\n\nmodel.compile(optimizer='adadelta',\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.summary()\n\n\n",
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        "keywords": "research model",
        "license": "MIT",
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        "name": "zhang2016dependency",
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