Tensorflow Fundamentals - Computation Graph Part 2

Let's continue our journey on Tensorflow's computation graph.

We will now make use of Tensorflow's variables. They are different from constant in that
1. they are mutable during the execution, i.e., they can change their values
2. they will store their state (value), which shall equal to that from the lastest execution

For example, you can define a counter variable that will increment its value on each execution:
counter = counter + 1

Below is the simple demo code for doing this in Tensorflow:

	import tensorflow as tf
	# counter init to 0
	counter = tf.Variable(0)
	# constant to increment counter by each execution
	one = tf.constant(1)
	# counter = counter + 1
	increment = tf.assign(counter, tf.add(counter, one))
	# required to initialize any tf's variables
	init = tf.global_variables_initializer()
	with tf.Session() as sess:
	# init all tf variables
	sess.run(init)
	# initial counter value
	print sess.run(counter)
	# increment 5 times
	for _ in range(5):
	# execute the actually increment
	sess.run(increment)
	print sess.run(counter)

view raw tf_computation_graph_p2.py hosted with ❤ by GitHub

The only catch here is that
1. tf.assign is another operation that will assign a new value to the variable on each execution (run)
2. one must initialize all variables

Running it will output
0
1
2
3
4
5

So far so good!