Metadata-Version: 1.1
Name: gradientzoo
Version: 0.8.4
Summary: Gradientzoo python bindings
Home-page: https://github.com/gradientzoo/python-gradientzoo
Author: Eric Florenzano
Author-email: support@gradientzoo.com
License: UNKNOWN
Description: Gradientzoo Python bindings
        ===========================
        
        .. image:: https://readthedocs.org/projects/python-gradientzoo/badge/?version=latest
        :target: http://python-gradientzoo.readthedocs.org/en/latest/?badge=latest
        :alt: Documentation Status
        
        This is a Python library for Gradientzoo's API - Version and share your trained
        neural network models.  Loading a pre-trained neural network is easy with
        Gradientzoo. Here's how easy it is to load a model with Tensorflow (full
        example below):
        
        .. code:: python
        
            import tensorflow as tf
            from gradientzoo.tensorflow import TensorflowGradientzoo
        
            # (build MNIST graph here)
        
            with tf.Session() as sess:
                # Load latest weights from Gradientzoo
                TensorflowGradientzoo('ericflo/mnist').load(sess)
        
                # Graph is now ready to use!
        
        
        Saving models is similarly straightforward:
        
        .. code:: python
        
            import tensorflow as tf
            from gradientzoo import TensorflowGradientzoo
        
            # (build MNIST graph here)
        
            with tf.Session() as sess:
                for epoch in xrange(6):
                    # Train the model...
        
                    # Save the updated weights out to Gradientzoo
                    TensorflowGradientzoo('ericflo/mnist').save(sess)
        
        
        Features
        --------
        
        Supports saving models in Keras_, variables in Tensorflow_, and networks in Lasagne_, and regular old files using Python with your framework of choice.
        
        
        Installation
        ------------
        
        You don't need this source code unless you want to modify the
        package. If you just want to use the Gradientzoo Python bindings, you
        should run:
        
            pip install --upgrade gradientzoo
        
        or
        
            easy_install --upgrade gradientzoo
        
        See http://www.pip-installer.org/en/latest/index.html for instructions
        on installing pip. If you are on a system with easy_install but not
        pip, you can use easy_install instead. If you're not using virtualenv,
        you may have to prefix those commands with `sudo`. You can learn more
        about virtualenv at http://www.virtualenv.org/
        
        To install from source, run:
        
            python setup.py install
        
        
        Documentation
        -------------
        
        Please see http://python-gradientzoo.readthedocs.org/ for the most up-to-date
        documentation or visit a project page to see project-specific instructions,
        e.g. https://www.gradientzoo.com/ericflo/mnist
        
        Setting up a Gradientzoo Account
        --------------------------------
        
        Sign up for Gradientzoo at https://www.gradientzoo.com/register
        
        Contribute
        ----------
        
        - Issue Tracker: https://github.com/gradientzoo/python-gradientzoo/issues
        - Source Code: https://github.com/gradientzoo/python-gradientzoo
        
        Support
        -------
        
        If you are having issues, please let us know at support@gradientzoo.com
        
        Full Tensorflow Example
        -----------------------
        
        .. code:: python
        
            import tensorflow as tf
            from tensorflow.examples.tutorials.mnist import input_data, mnist
            from gradientzoo.tensorflow import TensorflowGradientzoo
        
            learning_rate = 0.01
            batch_size = 100
        
            # Build MNIST graph
            images_placeholder = tf.placeholder(tf.float32,
                                                shape=(batch_size, mnist.IMAGE_PIXELS))
            labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
            logits = mnist.inference(images_placeholder, 128, 32)
            loss = mnist.loss(logits, labels_placeholder)
            train_op = mnist.training(loss, learning_rate)
            eval_correct = mnist.evaluation(logits, labels_placeholder)
        
            # Start a Tensorflow session
            with tf.Session() as sess:
                # Load latest weights from Gradientzoo
                TensorflowGradientzoo('ericflo/mnist').load(sess)
        
                # Read in some data
                data_sets = input_data.read_data_sets('data', False)
        
                # Test the trained network on the dataset
                true_count = 0
                for step in xrange(data_sets.test.num_examples // batch_size):
                    images_feed, labels_feed = data_sets.test.next_batch(batch_size, False)
        
                    true_count += sess.run(eval_correct, feed_dict={
                        images_placeholder: images_feed,
                        labels_placeholder: labels_feed,
                    })
        
                precision = true_count / float(data_sets.test.num_examples)
                print('Num Examples: %d  Num Correct: %d  Precision: %0.04f' %
                      (data_sets.test.num_examples, true_count, precision))
        
        .. _`gradientzoo.com/ericflo/mnist`: https://www.gradientzoo.com/ericflo/mnist
        .. _`readthedocs.org`: http://python-gradientzoo.readthedocs.org/en/latest/
        .. _Keras: http://keras.io/
        .. _Tensorflow: https://www.tensorflow.org/
        .. _Lasagne: http://lasagne.readthedocs.org/en/latest/
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
