Mayank Jain Production Engineer at Facebook Inc.

TensorFlow - Exercise 1 - Sum of two numbers

Exercise : Sum of two numbers

Given two number say 5 and 15 find the sum of these two numbers. You are given the numbers to add in advance and the numbers won’t change.

Excepted output 20

Caution : Solution for exercise is below. Please try solving problem by yourself before looking below

# Code starts here
import tensorflow as tf

# As we are told that numbers won't change 
# so it's safe to assume it as tensorflow constant
a = tf.constant(5,name="a")
b = tf.constant(15,name="b")

# Now we have to add a and b.
# tenorflow provides add function for same.
c = tf.add(a,b,name="c")

# Tensor flow creates graph and doesn't run the graph till
# you run the graph in a session. 
# So if we try printing value of c at this point
# we will get the output as a tensor and not actual value of c
# This is because value of c is not computed till we demand 
# value of c, (and this is done by )
print("Value of c before running tensor:",c)
Value of c before running tensor: Tensor("c_5:0", shape=(), dtype=int32)
# A new session is created using tf.Session() call.
sess = tf.Session()

# now we need to run graph we created
# c is passed as input to run as we need 
# to run graph till value of c is obtained.
output =
print("Value of c after running graph:",output)

# Once we are done we need to close the session.

# code ends here
Value of c after running graph: 20

While learning tensorflow I kept hearing that tensorflow creates graph, but it was hard to visualize. I used to wonder if I can see the graph which tensorflow creates. And the answer is YES. We can actually see the graph created by tensorflow.

Tensorflow provides a tool called Tensorboard which can be used to see the graph created by tensorflow. Please google and learn about tensorboard in detail. Refer: Tensorboard for details on same.

For now, Let’s check the graph created for above program by tensorboard. The graph created for above program is:


As it can be seen in the graph that c is dependent on a and b. So when we execute tensorflow evalutes c based on a and b. Tensor a and b are evaluted first and then c is computed based on value of a and b.

Let’s try last thing for this exercise. What happens if we define d and don’t use value of d at all. Consider code below

import tensorflow as tf

a = tf.constant(5,name="a")
b = tf.constant(15,name="b")
c = tf.add(a,b,name="c")
d = tf.sub(a,b,name="d")

sess = tf.Session()
output =;

In this case we are running session on c, so only c and all nodes on which c is dependent (a and b in our case) are evaluated. d tensor (which is subtraction of a and b) is not computed at all. To evaluate d we need either compute d in same or a new session like

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