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In other words, there are two single layer perceptron networks. Each perceptron produces a line. Knowing that there are just two lines required to represent the decision boundary tells us that the first hidden layer will have two hidden neurons. Up to this point, we have a single hidden layer with two hidden neurons.
Traditionally, neural networks only had three types of layers: hidden, input and output. These are all really the same type of layer if you just consider that input layers are fed from external data (not a previous layer) and output feed data to an external destination (not the next layer).
What are hidden layers?
Hidden layer(s) are the secret sauce of your network. They allow you to model complex data thanks to their nodes/neurons. They are “hidden” because the true values of their nodes are unknown in the training dataset. In fact, we only know the input and output. Each neural network has at least one hidden layer.
Choosing Hidden Layers If data is less complex and is having fewer dimensions or features then neural networks with 1 to 2 hidden layers would work. If data is having large dimensions or features then to get an optimum solution, 3 to 5 hidden layers can be used.
Hidden layers, simply put, are layers of mathematical functions each designed to produce an output specific to an intended result. Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. Each hidden layer function is specialized to produce a defined output.
What does each layer do in CNN?
Convolution Layers There are three types of layers that make up the CNN which are the convolutional layers, pooling layers, and fully-connected (FC) layers. When these layers are stacked, a CNN architecture will be formed.