For loop defining the network architecture

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% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% Created 26-Apr-2020 11:07:28
% This script assumes these variables are defined:
% Einleger_Binaer_Alle_inv_sortiert - input data.
% Auslenkung_Alle_inv - target data.
x = Einleger_Binaer_Alle_inv_Sortiert;
t = Auslenkung_Alle_inv;
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm'; % nicht Levenberg-Marquardt backpropagation, da schneller
% Create a Fitting Network
% hiddenLayerSize = 10;
% net_hiddenlayersize6_sortiert = fitnet(hiddenLayerSize,trainFcn);
% For more hidden layers, layer construction
hiddenLayer1Size = 10;
hiddenLayer2Size = 10;
net = fitnet([hiddenLayer1Size hiddenLayer2Size], trainFcn);
Now I have a neural network with two hidden layers. i want to expand it for more hidden layers.
Can I use a for-loop for layer construction? And if it is possible, how can I write it?

Accepted Answer

Bhavya Chopra
Bhavya Chopra on 8 Jul 2021
I understand that you want to create a neural network with multiple hidden layers instantiated using a for loop. The fitnet function can be provided a vector of hidden layer sizes. Assuming the same size for each hidden layer:
trainFcn = 'trainlm';
n = 5; % Number of hidden layers
s = 10; % Size of each hidden layer
layerSizes = ones(1, n)*s % Creating vector with layer sizes
net = fitnet(layerSizes, trainFcn);
Alternatively, if the layers have varying sizes, for instance in reducing powers of two:
trainFcn = 'trainlm';
n = 5; % Number of hidden layers
layerSizes = zeros(1,n);
for i = 1:n
layerSizes(i) = 2^(n-i+1);
net = fitnet(layerSizes, trainFcn);

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