How many hidden layers are necessary to approximate a continuous function?
1 hidden layer
The Universal Approximation Theorem states that a neural network with 1 hidden layer can approximate any continuous function for inputs within a specific range.
How many hidden layers are needed to approximate any reasonable behaved function?
Put simply, it states that a neural network with one hidden layer containing a sufficient but finite number of neurons can approximate any continuous function to a reasonable accuracy, under certain conditions for activation functions (namely, that they must be sigmoid-like).
What should the number of hidden neurons be?
The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. These three rules provide a starting point for you to consider.
How many hidden layers / neurons to use in Ann?
ANN is inspired by the biological neural network. For simplicity, in computer science, it is represented as a set of layers. These layers are categorized into three classes which are input, hidden, and output. Knowing the number of input and output layers and the number of their neurons is the easiest part.
Can a neural network approximate a hidden layer?
According to the Universal Approximation Theorem, a neural network with a single hidden layer can do exactly that. For our purposes, we will be looking only at fully connected neural networks with an input layer, a single hidden layer, and an output layer.
How is the number of neurons in a network determined?
Every network has a single input layer and a single output layer. The number of neurons in the input layer equals the number of input variables in the data being processed. The number of neurons in the output layer equals the number of outputs associated with each input.