How to create a fitnet neural network with multiple hidden layers?

I am new to using the machine learning toolboxes of MATLAB (but loving it so far!)

From a large data set I want to fit a neural network, to approximate the underlying unknown function. I have used the "Neural Net Fitting" app and generated a script with it which builds and trains my network. It all works, however the results are not good enough. I think the network is not complex enough to cover the non-linearities. So, I figued I'd add another hidden layer, but I can't get it to work.

The current code to produce the network is the following (which is the default):

trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.

% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize, trainFcn);

How would I modify this to add more hidden layers?

I am looking to get the classical Multi-Layer Perceptron (MLP) network, with potentially even more hidden layers:

[Junior Barrera]

Dear friends,

I would like to design a network, that I will call net_R similar to this one, but with the following charactersitics:

Design a basic module with four neurons:

w1x1 + w2x2 + k1<= 0

w1x1 + w2x2 + k2 > =0

-(1/w1)x1 + w2x2 + k3 <= 0

-(1/w1)x1 + w2x2 + k4 >=0

that is, net_R(x1,x2) = 1 <=> (x1,x2) is insided the rectangle nearned from a sample. Not that this 4 perceptrons network depends just of four parameters: (w1,w2,k1,k2,k3,k4). This should imply in less data due to the sharp

modeling that imply in smaller VC-dimension.

ii-design the netowork Net-4 that integrates 4 net_R(x1,x2) by the OR connective, that is,

Net-4(x1,x2) = 1 <=> net_R(y)(x1,x2)=1 for y=1,2,3 or 4,

y being the indides of the rectangle networks.

Thank you very much

Junior Barrera (Univrsity of SP - Brazil)

[Robert Roos]

I don't think this is related to the original question. I think you'd better start a new question.

Answer 1

[Patel Mounika]

You can add more hidden layers as shown below:

trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayer1Size = 10;
hiddenLayer2Size = 10;
net = fitnet([hiddenLayer1Size hiddenLayer2Size],  trainFcn);

This creates network of 2 hidden layers of size 10 each.

Hope this helps.

[Robert Roos]

Yup, that's exactly what I was looking for. Life is more simple than I thought :p

[meraj fouad]

Once again life becomes easy. Bless to the day it was asked.

[Roberto Williams Cedeño]

Thanks Patel.

[Roberto Williams Cedeño]

Thanks Patel.

[CARLOS MAURICIO RUIZ DIAZ]

Thanks mi friend, I had the same question!!!