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        "author": "Lene Preuss",
        "author_email": "lene.preuss@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.4",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# nn-wtf - Neural Networks Wrapper for TensorFlow\n\nnn-wtf aims at providing a convenience wrapper to Google's \n[TensorFlow](http://www.tensorflow.org/) machine learning library. \nIts focus is on making neural networks easy to set up, train and use.\n\nThe library is in pre-alpha right now and does not do anything seriously useful \nyet.\n\n## Installation\n\nnn-wtf runs under Python3.4 and above.\n\n### Dependencies\n\nYou need to install TensorFlow manually. The installation process depends on \nyour system. Install the version of TensorFlow built for Python 3.4. \n\nSee \nhttps://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#download-and-setup\nfor details.\n\nExample installation for Linux without GPU support:\n```\n$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0rc0-cp34-cp34m-linux_x86_64.whl\n```\n\n### NN-WTF itself\nSimple:\n```\n$ pip install nn_wtf\n```\n\n## Documentation\n\nSorry the documentation is absolutely minimal at this point. More useful\ndocumentation will be ready by the time this package reaches alpha status.\n\n### List of useful classes and methods\n\n* `NeuralNetworkGraph`: Base class for defining and training neural networks\n  * `__init__(self, input_size, layer_sizes, output_size, learning_rate)`\n  * `set_session(self, session=None)`\n  * `train(self, data_sets, max_steps, precision, steps_between_checks, run_as_check, batch_size)`\n  * `get_predictor().predict(input_data)`\n* `MNISTGraph`: Subclass of NeuralNetworkGraph suitable for working on MNIST data\n* `NeuralNetworkOptimizer`: Optimize geometry of a neural network for fast training\n  * `__init__( self, tested_network, input_size, output_size, training_precision,\n            layer_sizes, learning_rate, verbose, batch_size)`\n  * `brute_force_optimal_network_geometry(self, data_sets, max_steps)`\n\n### Usage example\n\nIf you want to try it on MNIST data, try this sample program:\n\n```python\nfrom nn_wtf.mnist_data_sets import MNISTDataSets\nfrom nn_wtf.mnist_graph import MNISTGraph\n\nimport tensorflow as tf\n\ndata_sets = MNISTDataSets('.')\ngraph = MNISTGraph(\n    learning_rate=0.1, layer_sizes=(64, 64, 16), train_dir='.'\n)\ngraph.train(data_sets, max_steps=5000, precision=0.95)\n\nimage_data = MNISTDataSets.read_one_image_from_url(\n    'http://github.com/lene/nn-wtf/blob/master/nn_wtf/data/7_from_test_set.raw?raw=true'\n)\nprediction = graph.get_predictor().predict(image_data)\nassert prediction == 7\n```\n\n>From there on, you are on your own for now. More functionality and documentation\nto come.\n\n\n",
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