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    "info": {
        "author": "Eric Florenzano",
        "author_email": "support@gradientzoo.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Gradientzoo Python bindings\n===========================\n\n.. image:: https://readthedocs.org/projects/python-gradientzoo/badge/?version=latest\n    :target: http://python-gradientzoo.readthedocs.org/en/latest/?badge=latest\n    :alt: Documentation Status\n\nThis is a Python library for Gradientzoo's API - Version and share your trained\nneural network models.  Loading a pre-trained neural network is easy with\nGradientzoo. Here's how easy it is to load a model with Tensorflow (full\nexample below):\n\n.. code:: python\n\n    import tensorflow as tf\n    from gradientzoo.tensorflow import TensorflowGradientzoo\n\n    # (build MNIST graph here)\n\n    with tf.Session() as sess:\n        # Load latest weights from Gradientzoo\n        TensorflowGradientzoo('ericflo/mnist').load(sess)\n\n        # Graph is now ready to use!\n\n\nSaving models is similarly straightforward:\n\n.. code:: python\n\n    import tensorflow as tf\n    from gradientzoo import TensorflowGradientzoo\n\n    # (build MNIST graph here)\n\n    with tf.Session() as sess:\n        for epoch in xrange(6):\n            # Train the model...\n\n            # Save the updated weights out to Gradientzoo\n            TensorflowGradientzoo('ericflo/mnist').save(sess)\n\n\nFeatures\n--------\n\nSupports saving models in Keras_, variables in Tensorflow_, and networks in Lasagne_, and regular old files using Python with your framework of choice.\n\n\nInstallation\n------------\n\nYou don't need this source code unless you want to modify the\npackage. If you just want to use the Gradientzoo Python bindings, you\nshould run:\n\n    pip install --upgrade gradientzoo\n\nor\n\n    easy_install --upgrade gradientzoo\n\nSee http://www.pip-installer.org/en/latest/index.html for instructions\non installing pip. If you are on a system with easy_install but not\npip, you can use easy_install instead. If you're not using virtualenv,\nyou may have to prefix those commands with `sudo`. You can learn more\nabout virtualenv at http://www.virtualenv.org/\n\nTo install from source, run:\n\n    python setup.py install\n\n\nDocumentation\n-------------\n\nPlease see http://python-gradientzoo.readthedocs.org/ for the most up-to-date\ndocumentation or visit a project page to see project-specific instructions,\ne.g. https://www.gradientzoo.com/ericflo/mnist\n\nSetting up a Gradientzoo Account\n--------------------------------\n\nSign up for Gradientzoo at https://www.gradientzoo.com/register\n\nContribute\n----------\n\n- Issue Tracker: https://github.com/gradientzoo/python-gradientzoo/issues\n- Source Code: https://github.com/gradientzoo/python-gradientzoo\n\nSupport\n-------\n\nIf you are having issues, please let us know at support@gradientzoo.com\n\nFull Tensorflow Example\n-----------------------\n\n.. code:: python\n\n    import tensorflow as tf\n    from tensorflow.examples.tutorials.mnist import input_data, mnist\n    from gradientzoo.tensorflow import TensorflowGradientzoo\n\n    learning_rate = 0.01\n    batch_size = 100\n\n    # Build MNIST graph\n    images_placeholder = tf.placeholder(tf.float32,\n                                        shape=(batch_size, mnist.IMAGE_PIXELS))\n    labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))\n    logits = mnist.inference(images_placeholder, 128, 32)\n    loss = mnist.loss(logits, labels_placeholder)\n    train_op = mnist.training(loss, learning_rate)\n    eval_correct = mnist.evaluation(logits, labels_placeholder)\n\n    # Start a Tensorflow session\n    with tf.Session() as sess:\n        # Load latest weights from Gradientzoo\n        TensorflowGradientzoo('ericflo/mnist').load(sess)\n\n        # Read in some data\n        data_sets = input_data.read_data_sets('data', False)\n\n        # Test the trained network on the dataset\n        true_count = 0\n        for step in xrange(data_sets.test.num_examples // batch_size):\n            images_feed, labels_feed = data_sets.test.next_batch(batch_size, False)\n\n            true_count += sess.run(eval_correct, feed_dict={\n                images_placeholder: images_feed,\n                labels_placeholder: labels_feed,\n            })\n\n        precision = true_count / float(data_sets.test.num_examples)\n        print('Num Examples: %d  Num Correct: %d  Precision: %0.04f' %\n              (data_sets.test.num_examples, true_count, precision))\n\n.. _`gradientzoo.com/ericflo/mnist`: https://www.gradientzoo.com/ericflo/mnist\n.. _`readthedocs.org`: http://python-gradientzoo.readthedocs.org/en/latest/\n.. _Keras: http://keras.io/\n.. _Tensorflow: https://www.tensorflow.org/\n.. _Lasagne: http://lasagne.readthedocs.org/en/latest/",
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