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        "author": "Grigory Malivenko",
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        "description": "# gluon2keras\nGluon to Keras deep neural network model converter\n\n[![Build Status](https://travis-ci.com/nerox8664/gluon2keras.svg?branch=master)](https://travis-ci.com/nerox8664/gluon2keras)\n[![GitHub License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n[![Python Version](https://img.shields.io/badge/python-2.7%2C3.6-lightgrey.svg)](https://github.com/nerox8664/gluon2keras)\n\n## Installation\n\n```\ngit clone https://github.com/nerox8664/gluon2keras\ncd gluon2keras\npip install -e .\n```\n\nor you can use `pip`:\n\n```\npip install gluon2keras\n```\n\n## Usage\n\n```\n# Get a model from gluon cv\nfrom gluoncv2.model_provider import get_model as glcv2_get_model\nnet = glcv2_get_model(\"xception\")\n\n# Make sure it's hybrid and initialized\nnet.hybridize()\nnet.collect_params().initialize()\n\n# Test input\ninput_np = np.random.uniform(0, 1, (1, 3, 299, 299))\ngluon_output = net(mx.nd.array(input_np))\n\n# Keras model\nk_model = gluon2keras(net, [(1, 3, 299, 299)], verbose=True, names='short')\nerror = check_error(gluon_output, k_model, input_np)\n```\n\n## Code snippets\nLook at the `tests` directory.\n\n## License\nThis software is covered by MIT License.",
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