TensorFlow Scope Decorator

blog_tensorflow_scope_decorator.py

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without
    parentheses if no arguments are provided. All arguments must be optional.
    """
    @functools.wraps(function)
    def decorator(*args, **kwargs):
        if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
            return function(args[0])
        else:
            return lambda wrapee: function(wrapee, *args, **kwargs)
    return decorator


@doublewrap
def define_scope(function, scope=None, *args, **kwargs):
    """
    A decorator for functions that define TensorFlow operations. The wrapped
    function will only be executed once. Subsequent calls to it will directly
    return the result so that operations are added to the graph only once.

    The operations added by the function live within a tf.variable_scope(). If
    this decorator is used with arguments, they will be forwarded to the
    variable scope. The scope name defaults to the name of the wrapped
    function.
    """
    attribute = '_cache_' + function.__name__
    name = scope or function.__name__
    @property
    @functools.wraps(function)
    def decorator(self):
        if not hasattr(self, attribute):
            with tf.variable_scope(name, *args, **kwargs):
                setattr(self, attribute, function(self))
        return getattr(self, attribute)
    return decorator


class Model:

    def __init__(self, image, label):
        self.image = image
        self.label = label
        self.prediction
        self.optimize
        self.error

    @define_scope(initializer=tf.contrib.slim.xavier_initializer())
    def prediction(self):
        x = self.image
        x = tf.contrib.slim.fully_connected(x, 200)
        x = tf.contrib.slim.fully_connected(x, 200)
        x = tf.contrib.slim.fully_connected(x, 10, tf.nn.softmax)
        return x

    @define_scope
    def optimize(self):
        logprob = tf.log(self.prediction + 1e-12)
        cross_entropy = -tf.reduce_sum(self.label * logprob)
        optimizer = tf.train.RMSPropOptimizer(0.03)
        return optimizer.minimize(cross_entropy)

    @define_scope
    def error(self):
        mistakes = tf.not_equal(
            tf.argmax(self.label, 1), tf.argmax(self.prediction, 1))
        return tf.reduce_mean(tf.cast(mistakes, tf.float32))


def main():
    mnist = input_data.read_data_sets('./mnist/', one_hot=True)
    image = tf.placeholder(tf.float32, [None, 784])
    label = tf.placeholder(tf.float32, [None, 10])
    model = Model(image, label)
    sess = tf.Session()
    sess.run(tf.initialize_all_variables())

    for _ in range(10):
      images, labels = mnist.test.images, mnist.test.labels
      error = sess.run(model.error, {image: images, label: labels})
      print('Test error {:6.2f}%'.format(100 * error))
      for _ in range(60):
        images, labels = mnist.train.next_batch(100)
        sess.run(model.optimize, {image: images, label: labels})


if __name__ == '__main__':
  main()

@rllin Have you found a way to use Saver with this model?
@danijar It would we very useful if you can incorporate the ability to save and load trained model using this class. Do you have some ideas on how this can be done?

Hey, I tried extending the idea of the decorator here to set the variable names as well

Thanks for this gist @danijar Could you please explain how the feed_dict in line 87 (or any similar line) works? Are the feed_dict keys same as the args in init ? Thanks.

I got that part! But I have another question now - Is it possible to define the attributes such as "optimize" in another file that imports this class? If yes, how? Thanks.

Hello, I changed the code a little bit.
I want to change the cost function dynamically in each iteration, the code is here https://github.com/jren2017/accessAccuracyFromLossFunc/blob/master/from_gist_example.py

I want to access the error of last iteration, use the error to calculate the cross-entropy, this sounds weird, but just an example.
Hope you can help have a look.
changed 65th row of the code:
logprob = tf.log(self.prediction + 1e-12) *(1-current_error) #Here changed ????????????

I get the error message below when I run the example. How do I fix it? It appears to be missing the _gru_ops library.
Great article. Thank you.

File "C:\ProgramData\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\framework\load_library.py", line 56, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)

NotFoundError: C:\ProgramData\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\contrib\rnn\python\ops_gru_ops.so not found