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Neural Network Foundations with TensorFlow 2.0
For a given input, several types of output can be computed, including a method
model.evaluate() used to compute the loss values, a method model.predict_
classes() used to compute category outputs, and a method model.predict_
proba() used to compute class probabilities.
A practical overview of backpropagation
Multi-layer perceptrons learn from training data through a process called
backpropagation. In this section, we will cover the basics while more details can be
found in Chapter 15, The Math behind Deep Learning. The process can be described as a
way of progressively correcting mistakes as soon as they are detected. Let's see how
this works.
Remember that each neural network layer has an associated set of weights that
determine the output values for a given set of inputs. Additionally, remember that
a neural network can have multiple hidden layers.
At the beginning, all the weights have some random assignment. Then, the net is
activated for each input in the training set: values are propagated forward from the
input stage through the hidden stages to the output stage where a prediction is
made. Note that we've kept Figure 38 simple by only representing a few values with
green dotted lines but in reality all the values are propagated forward through the
network:
Figure 38: Forward step in backpropagation
Since we know the true observed value in the training set, it is possible to calculate
the error made in prediction. The key intuition for backtracking is to propagate the
error back (see Figure 39), using an appropriate optimizer algorithm such as gradient
descent to adjust the neural network weights with the goal of reducing the error
(again, for the sake of simplicity, only a few error values are represented here):
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