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You must be wondering why we have to write so many lines of code for a simple vector addition or to print a small message. Well, you could have very conveniently done this work in a one-liner:

print(tf.Session().run(tf.add(tf.constant([1,2,3,4]),tf.constant([2,1,5,3])))) 

Writing this type of code not only affects the computational graph but can be memory expensive when the same operation ( OP) is performed repeatedly in a for loop. Making a habit of explicitly defining all tensor and operation objects not only makes the code more readable but also helps you visualize the computational graph in a cleaner manner.

Visualizing the graph using TensorBoard is one of the most useful capabilities of TensorFlow, especially when building complicated neural networks. The computational graph that we built can be viewed with the help of Graph Object.

If you are working on Jupyter Notebook or Python shell, it is more convenient to use tf.InteractiveSession instead of tf.Session. InteractiveSession makes itself the default session so that you can directly call run the tensor Object using eval() without explicitly calling the session, as described in the following example code:

sess = tf.InteractiveSession() 
 
v_1 = tf.constant([1,2,3,4]) 
v_2 = tf.constant([2,1,5,3]) 
 
v_add = tf.add(v_1,v_2) 
 
print(v_add.eval()) 
 
sess.close()