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Gradientzoo python bindings

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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):

import tensorflow as tf
from gradientzoo.tensorflow import TensorflowGradientzoo

# (build MNIST graph here)

with tf.Session() as sess:
    # Load latest weights from Gradientzoo

    # Graph is now ready to use!

Saving models is similarly straightforward:

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


Supports saving models in Keras, variables in Tensorflow, and networks in Lasagne, and regular old files using Python with your framework of choice.


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


easy_install –upgrade gradientzoo

See 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

To install from source, run:

python install


Please see for the most up-to-date documentation or visit a project page to see project-specific instructions, e.g.

Setting up a Gradientzoo Account

Sign up for Gradientzoo at



If you are having issues, please let us know at

Full Tensorflow Example

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 =, learning_rate)
eval_correct = mnist.evaluation(logits, labels_placeholder)

# Start a Tensorflow session
with tf.Session() as sess:
    # Load latest weights from Gradientzoo

    # 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 +=, 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))

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