I plan to use the Nguyen-Widrow Algorithm for an NN with **multiple hidden layers**. While researching, I found a lot of ambiguities and I wish to clarify them.

The following is pseudo-code for the Nguyen-Widrow Algorithm

Initialize all weight of hidden layers with random values For each hidden layer{ beta = 0.7 * Math.pow(hiddenNeurons, 1.0 / number of inputs); For each synapse{ For each weight{ Adjust weight by dividing by norm of weight for neuron and * multiplying by a beta value } } }

**I just wanted to clarify whether the value of hiddenNeurons is the size of the particular hidden layer or the size of all the hidden layers within the network.** I got mixed up by viewing various sources.

In other words, if I have a network (3-2-2-2-3) *(index 0 is the input layer, index 4 is the output layer)*, would the value hiddenNeurons be:

NumberOfNeuronsInLayer(1) + NumberOfNeuronsInLayer(2) + NumberOfNeuronsInLaer(3)

Or just

NumberOfNeuronsInLayer(i), where i is the current Layer I am at

EDIT:

So, the hiddenNeurons value would be the size of the current hidden layer, and the input value would be the size of the previously hidden layer?