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    "info": {
        "author": "Idein Inc.",
        "author_email": "n.ohkawa@idein.jp",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3 :: Only"
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        "description": "# nnoir-chainer\n\nChainer Model from/to NNOIR converter\n\n## Install\n\n```\npip install nnoir-chainer\n```\n\n## Example\n\n### Import NNOIR\n\n```\nimport chainer\nfrom nnoir_chainer import NNOIRFunction\nm = NNOIRFunction('nnoir_file_path')\nx = chainer.Variable(np_array)\nwith chainer.using_config('train', False):\n    y = m(x)\n    print(y)\n```\n\n### Export NNOIR\n\n```\nm = model.CNN()\nchainer.serializers.load_npz('cnn.model', L.Classifier(m))\nwith chainer.using_config('train', False):\n    x = chainer.Variable(np.zeros((1, 28*28)).astype(np.float32))\n    y = m(x)\n    g = nnoir_chainer.Graph(m, (x,), (y,))\n    result = g.to_nnoir()\n    with open('model.nnoir', 'w') as f:\n        f.buffer.write(result)\n```\n\nThese layers supported by nnoir-chainer exporter.\n\n* chainer.links\n    * BatchNormalization\n    * Bias\n    * Linear\n    * Convolution2D (DepthwiseConvolution2D, DilatedConvolution2D)\n    * Scale\n* chainer.function\n    * Add\n    * AddConstant\n    * AveragePooling2D\n    * Concat\n    * Dropout\n    * ELU\n    * LeakyReLU\n    * MaxPooling2D\n    * Mul\n    * MulConstant\n    * Pad\n    * ReLU\n    * Reshape\n    * Sigmoid\n    * Softmax\n    * Tanh\n    * Transpose\n    * Unpooling2D\n\n\n",
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