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Remember that a neural network can have multiple hidden layers, as well as one
input layer and one output layer.
Chapter 15
In addition to that, recall from Chapter 1, Neural Network Foundations with
TensorFlow 2.0, that backpropagation can be described as a way of progressively
correcting mistakes as soon as they are detected. In order to reduce the errors
made by a neural network, we must train the network. The training needs a dataset
including input values and the corresponding true output value. We want to use
the network for predicting the output as close as possible to the true output value.
The key intuition of the backpropagation algorithm is to update the weights of the
connections based on the measured error at the output neuron(s). In the remainder of
this section, we will explain how to formalize this intuition.
When backpropagation starts, 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 keep the following figure simple by only
representing a few values with green dotted lines, but in reality all the values are
propagated forward through the network):
Figure 9: 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.
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