TensorFlow code structure

The TensorFlow programming model signifies how to structure your predictive models. A TensorFlow program is generally divided into four phases when you have imported the TensorFlow library:

• Construction of the computational graph that involves some operations on tensors (we will see what a tensor is soon)
• Creation of a session
• Running a session; performed for the operations defined in the graph
• Computation for data collection and analysis

These main phases define the programming model in TensorFlow. Consider the following example, in which we want to multiply two numbers:

import tensorflow as tf # Import TensorFlow

x = tf.constant(8) # X op
y = tf.constant(9) # Y op
z = tf.multiply(x, y) # New op Z

sess = tf.Session() # Create TensorFlow session

out_z = sess.run(z) # execute Z op
sess.close() # Close TensorFlow session
print('The multiplication of x and y: %d' % out_z)# print result

The preceding code segment can be represented by the following figure:

Figure...