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    "info": {
        "author": "Duc Nguyen",
        "author_email": "adnguyen@yonsei.ac.kr",
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            "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3.5",
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        "description": "# Introduction\n![Python - Version](https://img.shields.io/pypi/pyversions/neuralnet-pytorch.svg)\n[![PyPI - Version](https://img.shields.io/pypi/v/neuralnet-pytorch.svg)](https://pypi.org/project/neuralnet-pytorch/)\n[![PyPI - Wheel](https://img.shields.io/pypi/wheel/neuralnet-pytorch.svg)](https://pypi.org/project/neuralnet-pytorch/)\n[![Github - Tag](https://img.shields.io/github/tag/justanhduc/neuralnet-pytorch.svg)](https://github.com/justanhduc/neuralnet-pytorch/releases/tag/rel-0.0.4)\n[![License](https://img.shields.io/github/license/justanhduc/neuralnet-pytorch.svg)](https://github.com/justanhduc/neuralnet-pytorch/blob/master/LICENSE.txt)\n\n__A high level framework for general purpose neural networks in Pytorch.__\n\nPersonally, going from Theano to Pytorch is pretty much like time traveling from 90s to the modern day. \nHowever, despite a lot of bells and whistles, I still feel there are some missing elements from Pytorch \nwhich are confirmed to be never added to the library. \nTherefore, this library is written to add more features to the current magical Pytorch. All the modules here\ndirectly subclass the corresponding modules from Pytorch, so everything should still be familiar. For example, the \nfollowing snippet in Pytorch\n\n```\nfrom torch import nn\nmodel = nn.Sequential(\n    nn.Conv2d(1, 20, 5, padding=2),\n    nn.ReLU(),\n    nn.Conv2d(20, 64, 5, padding=2),\n    nn.ReLU()\n)\n```\n\ncan be rewritten in Neuralnet-pytorch as \n```\nimport neuralnet_pytorch as nnt\nmodel = nnt.Sequential(\n    nnt.Conv2d(1, 20, 5, padding='half', activation='relu'),\n    nnt.Conv2d(20, 64, 5, padding='half', activation='relu')\n)\n```\n\nwhich is the same as the native Pytorch, or \n\n```\nimport neuralnet_pytorch as nnt\nmodel = nnt.Sequential(input_shape=1)\nmodel.add_module('conv1', nnt.Conv2d(model.output_shape, 20, 5, padding='half', activation='relu'))\nmodel.add_module('conv2', nnt.Conv2d(model.output_shape, 64, 5, padding='half', activation='relu'))\n```\nwhich frees you from a lot of memorizations and manual calculations when adding one layer on top of another. \nTheano folks will also find some reminiscence as many functions are highly inspired by Theano.  \n\n# Requirements\n\n[Pytorch](https://pytorch.org/) >= 1.0.0\n\n[Matplotlib](https://matplotlib.org/)\n\n[Visdom](https://github.com/facebookresearch/visdom)\n\n[Tensorboard](https://www.tensorflow.org/tensorboard)\n\n[Gin-config](https://github.com/google/gin-config) (optional)\n\n# Installation\n\nStable version\n```\npip install --upgrade neuralnet-pytorch\n```\n\nBleeding-edge version\n\n```\npip install git+git://github.com/justanhduc/neuralnet-pytorch.git@master\n```\n\nTo install the version with some collected Cuda/C++ ops, use\n\n```\npip install git+git://github.com/justanhduc/neuralnet-pytorch.git@fancy\n```\n\n# Usages\n\nThe manual reference is still under development and is available at https://neuralnet-pytorch.readthedocs.io.\n\n# TODO\n\n- [x] Adding introduction and installation \n- [x] Writing documentations\n- [ ] Adding examples\n\n# Disclaimer\n\nThis package is a product from my little free time during my PhD, \nso most but not all the written modules are properly checked. \nNo replacements or refunds for buggy performance. \nAll PRs are welcome. \n\n# Acknowledgements\n\nThe CUDA Chamfer distance is taken from the [AtlasNet](https://github.com/ThibaultGROUEIX/AtlasNet) repo.\n\nThe AdaBound optimizer is taken from its [official repo](https://github.com/Luolc/AdaBound).\n\nThe adapted Gin for Pytorch code is taken from [Gin-config](https://github.com/google/gin-config).\n\nThe monitor scheme is inspired from [WGAN](https://github.com/igul222/improved_wgan_training).\n\n\n",
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