Theano function equivalent in Tensorflow - tensorflow

Question

I'm wonder wrt this topic
I want to resolve update issue in Theano.function with this lazy tensorflow constrution:
class TensorFlowTheanoFunction(object):
def __init__(self, inputs, outputs, session):
self._inputs = inputs
self._outputs = outputs
self.session = session
def __call__(self, *args, **kwargs):
feeds = {}
for (argpos, arg) in enumerate(args):
feeds[self._inputs[argpos]] = arg
return self.session.run(self._outputs, feeds)
If I want to pass an update argument (like in Theano) how I can modify this lazy call?
I just want that this can also work in tensorflow:
self.new = theano.function([], [], updates=zip(old_params, params))

Answer 1

Just modifying Yaroslav's code from that thread to use tf.assign, with a control dependency to make sure the outputs are computed before the assignments happen:
import tensorflow as tf
class TensorFlowTheanoFunction(object):
def __init__(self, inputs, outputs, updates=()):
self._inputs = inputs
self._outputs = outputs
self._updates = updates
def __call__(self, *args, **kwargs):
feeds = {}
for (argpos, arg) in enumerate(args):
feeds[self._inputs[argpos]] = arg
try:
outputs_identity = [tf.identity(output) for output in self._outputs]
output_is_list = True
except TypeError:
outputs_identity = [tf.identity(self._outputs)]
output_is_list = False
with tf.control_dependencies(outputs_identity):
assign_ops = [tf.assign(variable, replacement)
for variable, replacement in self._updates]
outputs_list = tf.get_default_session().run(
outputs_identity + assign_ops, feeds)[:len(outputs_identity)]
if output_is_list:
return outputs_list
else:
assert len(outputs_list) == 1
return outputs_list[0]
a = tf.placeholder(dtype=tf.int32)
b = tf.placeholder(dtype=tf.int32)
variable = tf.get_variable(
"variable", shape=[], dtype=tf.int32, initializer=tf.zeros_initializer)
c = a + b + variable
d = a - b
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
f = TensorFlowTheanoFunction([a, b], [c, d], updates=[(variable, variable + 1)])
print f(1, 2)
print f(1, 2)
print f(0, 2)
f = TensorFlowTheanoFunction([a, b], c, updates=[(variable, variable + 1)])
print f(1, 2)
print f(1, 2)
print f(0, 2)
This updates the variable at each iteration:
[3, -1]
[4, -1]
[4, -2]
6
7
7