What does tf control Dependencies do

This recipe explains what does tf control Dependencies do

Recipe Objective

What does tf.control_Dependencies do?

The control dependencies is a system in which whatever operations we create under the "with" block it adds dependencies. Especially what we specify in the argument to "control_dependencies" which is assure to be checked out before anything we define in the "with" block. Lets understand this practically.

Step 1 - Import library

import tensorflow as tf

Step 2 - Perform the task

graph = tf.Graph() with graph.as_default(): a = tf.constant([0]) for i in range(10): a = a + 1 with tf.control_dependencies([a]): c = tf.identity(a) sess = tf.compat.v1.Session(graph=graph) sess.run(c)

array([10], dtype=int32)

Here we have taken Graph to run the session, and had done all the operations inside the graph. After that in "graph.as_default" which is set as default for operation we have defined tensor as "a" and then we have taken a for loop in which we have declare "range[10]", in which we are iterating the tensor "a". Then we have taken a with block for control dependencies in that we have identified our tensor and then run with the help of "Session".

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