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 resultThe preceding code segment can be represented by the following figure:
Figure 5: A simple...