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Note that a hyperplane is a subspace whose dimension is one less than that of its
ambient space. See Figure 3 for an example:
Chapter 1
Figure 3: An example of a hyperplane
In other words, this is a very simple but effective algorithm! For example, given three
input features, the amounts of red, green, and blue in a color, the perceptron could
try to decide whether the color is white or not.
Note that the perceptron cannot express a "maybe" answer. It can answer "yes" (1)
or "no" (0), if we understand how to define w and b. This is the "training" process
that will be discussed in the following sections.
A first example of TensorFlow 2.0 code
There are three ways of creating a model in tf.keras: Sequential API , Functional
API, and Model subclassing. In this chapter we will use the simplest one,
Sequential(), while the other two are discussed in Chapter 2, TensorFlow 1.x and 2.x.
A Sequential() model is a linear pipeline (a stack) of neural network layers. This
code fragment defines a single layer with 10 artificial neurons that expects 784 input
variables (also known as features). Note that the net is "dense," meaning that each
neuron in a layer is connected to all neurons located in the previous layer, and to all
the neurons in the following layer:
import tensorflow as tf
from tensorflow import keras
NB_CLASSES = 10
RESHAPED = 784
model = tf.keras.models.Sequential()
model.add(keras.layers.Dense(NB_CLASSES,
input_shape=(RESHAPED,), kernel_initializer='zeros',
name='dense_layer', activation='softmax'))
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