How to Create a Graph and run the Session in Tensorflow : 3 Steps

Well! Tensorflow works in such a way that we need to create graph . Tensorflow can distribute the graph in multiple chunks. Now Tensorflow handles the computation in distributive way . Actually these chunks can be distributed among various computing devices and run parallel . In this article we will see , How to create a Graph and run the session in Tensorflow : 3 Steps

How to create a Graph and run the session in Tensorflow-

There is a prerequisites of tensorflow installation . You may use the below command if they are not already installed –

pip3 install --upgrade tensorflow                   ### CPU version 

pip3 install --upgrade tensorflow-gpu               ### GPU version

Step 1 : In the first place , Import tensorflow module .

import tensorflow as tf

Step 2 : Lets Define Variable and create graph in tensorflow .

x = tf.Variable(2, name="x")
y = tf.Variable(4, name="y")
f = x*x + y*y + 2

Lets move further .

Step 3 : Now Create session ,initialize the variable and execute the graph .

sess = tf.Session()
sess.run(x.initializer)
sess.run(y.initializer)
result = sess.run(f)
print(result)
sess.close()

Actually The first two steps are Construction phase and the last step is execution phase . You will get the below output when you put the parts of code in above step together .

How to optimize graph creation and execution in Tensorflow –

The above code is enough to create a graph and run into session . Still you can cut down some line of code using below tips –

1. global_variables_initializer() – Using the above function , will save you from initializing each variable in session . Please refer the below code –

#Graph creation remain same

x = tf.Variable(2, name="x")
y = tf.Variable(4, name="y")
f = x*x + y*y + 2

#Graph execution
init = tf.global_variables_initializer() # prepare an init node
with tf.Session() as sess:
init.run() # actually initialize all the variables
result = f.eval()
print(result)

Output –

2. InteractiveSession() – In fact InteractiveSession is helpful gain to reduce line of cede in tensorflow as It set the default session as currently created InteractiveSession automatically . Please do not forget to close it after finishing all computation .Lets see the implementation here –

x = tf.Variable(2, name="x")
y = tf.Variable(4, name="y")
f = x*x + y*y + 2
init = tf.global_variables_initializer()
sess = tf.InteractiveSession()
init.run()
result = f.eval()
print(result)
sess.close()

Output-