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TensorFlow 1.x and 2.x
The intent of this chapter is to explain the differences between TensorFlow 1.x
and TensorFlow 2.0. We'll start by reviewing the traditional programming paradigm
for 1.x and then we'll move on to all the new features and paradigms available in 2.x.
Understanding TensorFlow 1.x
It is generally the tradition that the first program one learns to write in any computer
language is "hello world." We maintain the convention in this book! Let's begin with
a Hello World program:
import tensorflow as tf
message = tf.constant('Welcome to the exciting world of Deep Neural
Networks!')
with tf.Session() as sess:
print(sess.run(message).decode())
Let us go in depth into this simple code. The first line imports tensorflow. The
second line defines the message using tf.constant. The third line defines the
Session() using with, and the fourth runs the session using run(). Note that
this tells us that the result is a "byte string." In order to remove string quotes and
b (for byte) we use the method decode().
TensorFlow 1.x computational graph program
structure
TensorFlow 1.x is unlike other programming languages. We first need to build
a blueprint of whatever neural network we want to create. This is accomplished
by dividing the program into two separate parts: a definition of a computational
graph, and its execution.
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