Python Deep Learning - Second Edition

An introduction to layers

A neural network can have an indefinite number of neurons, which are organized in interconnected layers. The input layer represents the dataset and the initial conditions. For example, if the input is a grayscale image, the output of each neuron in the input layer is the intensity of one pixel of the image. For this very reason, we don't generally count the input layer as a part of the other layers. When we say 1-layer net, we actually mean that it is a simple network with just a single layer, the output, in addition to the input layer.

Unlike the examples we've seen so far, the output layer can have more than one neuron. This is especially useful in classification, where each output neuron represents one class. ...