Variables are TensorFlow objects that hold and update parameters. A variable must be initialized; also you can save and restore it to analyze your code.

Variables are created by the tf.Variable() statement.

In the following example, we want to count the numbers from 1 to 10:

import tensorflow as tf

We create a variable that will be initialized to the scalar value 0:

value = tf.Variable(0,name="value")

The assign() and add() operators are just nodes of the computation graph so they do not execute the assignment until the session is run:

one = tf.constant(1) 
new_value = tf.add(value,one) 
update_value=tf.assign(value,new_value) 

initialize_var = tf.global_variables_initializer()

We can instantiate the computation graph:

with tf.Session() as sess: 
sess.run(initialize_var) 
    print(sess.run(value)) 
    for _ in range(10): 
        sess.run(update_value) 
        print(sess.run(value))

Let's recall that a tensor object is a symbolic handle to the result of an operation, but does not actually hold the values of the operation's output:

>>> 
0 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
>>>