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        "author": "TFLearn contributors",
        "author_email": "aymeric.damien@gmail.com",
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        "classifiers": [
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        "description": "|Build Status| |PyPI version| |License| |Join the chat at\r\nhttps://gitter.im/einsteinsci/betterbeginnings|\r\n\r\nTFLearn: Deep learning library featuring a higher-level API for TensorFlow.\r\n===========================================================================\r\n\r\nTFlearn is a modular and transparent deep learning library built on top\r\nof Tensorflow. It was designed to provide a higher-level API to\r\nTensorFlow in order to facilitate and speed-up experimentations, while\r\nremaining fully transparent and compatible with it.\r\n\r\nTFLearn features include:\r\n\r\n-  Easy-to-use and understand high-level API for implementing deep\r\n   neural networks, with tutorial and examples.\r\n-  Fast prototyping through highly modular built-in neural network\r\n   layers, regularizers, optimizers, metrics\u2026\r\n-  Full transparency over Tensorflow. All functions are built over\r\n   tensors and can be used independently of TFLearn.\r\n-  Powerful helper functions to train any TensorFlow graph, with support\r\n   of multiple inputs, outputs and optimizers.\r\n-  Easy and beautiful graph visualization, with details about weights,\r\n   gradients, activations and more\u2026\r\n-  Effortless device placement for using multiple CPU/GPU.\r\n\r\nThe high-level API currently supports most of recent deep learning\r\nmodels, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual\r\nnetworks, Generative networks\u2026 In the future, TFLearn is also intended\r\nto stay up-to-date with latest deep learning techniques.\r\n\r\nNote: Latest TFLearn (v0.3) is only compatible with TensorFlow v1.0 and\r\nover.\r\n\r\nOverview\r\n--------\r\n\r\n.. code:: python\r\n\r\n    # Classification\r\n    tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)\r\n\r\n    net = tflearn.input_data(shape=[None, 784])\r\n    net = tflearn.fully_connected(net, 64)\r\n    net = tflearn.dropout(net, 0.5)\r\n    net = tflearn.fully_connected(net, 10, activation='softmax')\r\n    net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')\r\n\r\n    model = tflearn.DNN(net)\r\n    model.fit(X, Y)\r\n\r\n.. code:: python\r\n\r\n    # Sequence Generation\r\n    net = tflearn.input_data(shape=[None, 100, 5000])\r\n    net = tflearn.lstm(net, 64)\r\n    net = tflearn.dropout(net, 0.5)\r\n    net = tflearn.fully_connected(net, 5000, activation='softmax')\r\n    net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')\r\n\r\n    model = tflearn.SequenceGenerator(net, dictionary=idx, seq_maxlen=100)\r\n    model.fit(X, Y)\r\n    model.generate(50, temperature=1.0)\r\n\r\nThere are many more examples available *`here`_*.\r\n\r\nInstallation\r\n------------\r\n\r\n**TensorFlow Installation**\r\n\r\nTFLearn requires Tensorflow (version 1.0+) to be installed.\r\n\r\nTo install TensorFlow, simply run:\r\n\r\n::\r\n\r\n    pip install tensorflow\r\n\r\nor, with GPU-support:\r\n\r\n::\r\n\r\n    pip install tensorflow-gpu\r\n\r\nThere are many more examples available here:\r\nhttp://tflearn.org/examples.\r\n\r\nInstallation\r\n------------\r\n\r\n**TensorFlow Installation**\r\n\r\nTFLearn requires Tensorflow (version 1.0+) to be installed.\r\n\r\nTo install TensorFlow, simply run:\r\n\r\n::\r\n\r\n    pip install tensorflow\r\n\r\nor, with GPU-support:\r\n\r\n::\r\n\r\n    pip install tensorflow-gpu\r\n\r\nFor more details see:\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get\\_started/os\\_setup.md\r\n\r\n**TFLearn Installation**\r\n\r\nTo install TFLearn, the easiest way is to run\r\n\r\nFor the bleeding edge version (recommended):\r\n\r\n.. code:: python\r\n\r\n    pip install git+https://github.com/tflearn/tflearn.git\r\n\r\nFor the latest stable version:\r\n\r\n.. code:: python\r\n\r\n    pip install tflearn\r\n\r\nOtherwise, you can also install from source by running (from source\r\nfolder):\r\n\r\n.. code:: python\r\n\r\n    python setup.py install\r\n\r\n-  For more details, please see the `Installation Guide`_.\r\n\r\nGetting Started\r\n---------------\r\n\r\nSee `Getting Started with TFLearn`_ to learn about TFLearn basic\r\nfunctionalities or start browsing `TFLearn Tutorials`_.\r\n\r\nExamples\r\n--------\r\n\r\nThere are many neural network implementation available, see `Examples`_.\r\n\r\nDocumentation\r\n-------------\r\n\r\nhttp://tflearn.org/doc_index\r\n\r\nContributions\r\n-------------\r\n\r\nThis is the first release of TFLearn, if you find any bug, please report\r\nit in the GitHub issues section.\r\n\r\nImprovements and requests for new features are more than welcome! Do not\r\nhesitate to twist and tweak TFLearn, and send pull-requests.\r\n\r\nFor more info: `Contribute to TFLearn`_.\r\n\r\nLicense\r\n-------\r\n\r\nMIT License\r\n\r\n.. _here: http://tflearn.org/examples\r\n\r\n.. |Build Status| image:: https://travis-ci.org/tflearn/tflearn.svg?branch=master\r\n   :target: https://travis-ci.org/tflearn/tflearn\r\n.. |PyPI version| image:: https://badge.fury.io/py/tflearn.svg\r\n   :target: https://badge.fury.io/py/tflearn\r\n.. |License| image:: https://img.shields.io/badge/license-MIT-blue.svg\r\n   :target: LICENSE\r\n.. |Join the chat at https://gitter.im/einsteinsci/betterbeginnings| image:: https://badges.gitter.im/tflearn/tflearn.svg\r\n   :target: https://gitter.im/tflearn/tflearn?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\r\n.. _Installation Guide: http://tflearn.org/installation\r\n.. _Getting Started with TFLearn: http://tflearn.org/getting_started\r\n.. _TFLearn Tutorials: http://tflearn.org/tutorials\r\n.. _Examples: http://tflearn.org/examples\r\n.. _Contribute to TFLearn: http://tflearn.org/contributions",
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