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TensorFlow Scope Decorator
# Working example for my blog post at:
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 not arguments are provided. All arguments must be optional.
def decorator(*args, **kwargs):
if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
return function(args[0])
return lambda wrapee: function(wrapee, *args, **kwargs)
return decorator
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
attribute = '_cache_' + function.__name__
name = scope or function.__name__
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
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
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)
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()
for _ in range(10):
images, labels = mnist.test.images, mnist.test.labels
error =, {image: images, label: labels})
print('Test error {:6.2f}%'.format(100 * error))
for _ in range(60):
images, labels = mnist.train.next_batch(100), {image: images, label: labels})
if __name__ == '__main__':
lfwin commented Jul 15, 2016

I debuged this code and found that:
1 if set breakpoint at line number 15 'setattr(self, attribute, function(self))' which is execute in Model initilization ' model = Model(data, target)' at line number 53, can step in this line, but if set breakpoint before excecution this set attribute line such as line num 52 or line num 25 and can not step into this set attribute line, why?
2 after executing to set attribute self.optimize at line number 25 and befor excuting self.error, self has three attributes, self._optimize, self._error, self._prediction, i don't know how this be generated?

danijar commented Oct 4, 2016

Hi @lfwin, I don't know why your debugger can't step into setattr() statements. Maybe because it's a built-in function. Regarding your second question, that's the intention of the decorator. Please read my blog post for an explanation, the URL is at the beginning of the file.


Hi -- is there a license on this code? I'd like to use it in a package that is Apache 2.0 licensed. Happy to have a dependency on it if you prefer to package it up instead.

rllin commented Jan 26, 2017

Great design! Any idea how it works with tensorflow saver? How would you restore a checkpoint of a session that's baked into this class?

mihaic commented Jan 27, 2017

@danijar, is it OK to use this code in a project licensed under Apache 2.0?


Hi -- will "define_scope" work when the wrapped function have argument others than self? for example, if i try

def prediction(self, batch_size):
and I get "TypeError: prediction() missing 1 required positional argument"

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