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Chapter 1
Here's how the code is written in TensorFlow 2.0 to achieve the same results:
import tensorflow as tf
W = tf.Variable(tf.ones(shape=(2,2)), name="W")
b = tf.Variable(tf.zeros(shape=(2)), name="b")
@tf.function
def model(x):
return W * x + b
out_a = model([1,0])
print(out_a)
In this case, we have eight lines in total and the code looks cleaner and nicer. Indeed,
the key idea of TensorFlow 2.0 is to make TensorFlow easier to learn and to apply.
If you have started with TensorFlow 2.0 and have never seen TensorFlow 1.x, then
you are lucky. If you are already familiar with 1.x, then it is important to understand
the differences and you need to be ready to rewrite your code with some help from
automatic tools for migration, as discussed in Chapter 2, TensorFlow 1.x and 2.x. Before
that, let's start by introducing neural networks–one of the most powerful learning
paradigms supported by TensorFlow.
Introduction to neural networks
Artificial neural networks (briefly, "nets" or ANNs) represent a class of machine
learning models loosely inspired by studies about the central nervous systems of
mammals. Each ANN is made up of several interconnected "neurons," organized in
"layers." Neurons in one layer pass messages to neurons in the next layer (they "fire,"
in jargon terms) and this is how the network computes things. Initial studies were
started in the early 50's with the introduction of the "perceptron" [1], a two-layer
network used for simple operations, and further expanded in the late 60's with the
introduction of the "back-propagation" algorithm used for efficient multi-layer
network training (according to [2], [3]).
Some studies argue that these techniques have roots dating further
back than normally cited[4].
Neural networks were a topic of intensive academic studies up until the 80's, at
which point other, simpler approaches became more relevant. However, there
has been a resurgence of interest starting in the mid 2000's, mainly thanks to three
factors: a breakthrough fast learning algorithm proposed by G. Hinton [3], [5], [6];
the introduction of GPUs around 2011 for massive numeric computation; and the
availability of big collections of data for training.
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