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# How I can add more hidden layers on the nftool code that I exported from the nnstart GUI?

Since I don’t know much about how to implement a network using command line, I tried using the GUI from NNSTART and exported the code so I could try to figure out how to make the changes I need. the problems is that I don’t how to add more layers/neurons, even more ephocs.

Here is the code I got from my first attempt:

% Solve an Input-Output Fitting problem with a Neural Network

% Script generated by Neural Fitting app

% Created 13-Sep-2017 20:47:36

%

% This script assumes these variables are defined:

%

% Input_train - input data.

% Target_train - target data.x = Input_train;

t = Target_train;% 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'; % Levenberg-Marquardt backpropagation.% Create a Fitting Network

hiddenLayerSize = 23;

net = fitnet(hiddenLayerSize,trainFcn);% Choose Input and Output Pre/Post-Processing Functions

% For a list of all processing functions type: help nnprocess

net.input.processFcns = {'removeconstantrows','mapminmax'};

net.output.processFcns = {'removeconstantrows','mapminmax'};% Setup Division of Data for Training, Validation, Testing

% For a list of all data division functions type: help nndivide

net.divideFcn = 'dividerand'; % Divide data randomly

net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 80/100;

net.divideParam.valRatio = 10/100;

net.divideParam.testRatio = 10/100;% Choose a Performance Function

% For a list of all performance functions type: help nnperformance

net.performFcn = 'mse'; % Mean Squared Error% Choose Plot Functions

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'};% Train the Network

[net,tr] = train(net,x,t);% Test the Network

y = net(x);

e = gsubtract(t,y);

performance = perform(net,t,y)% Recalculate Training, Validation and Test Performance

trainTargets = t .* tr.trainMask{1};

valTargets = t .* tr.valMask{1};

testTargets = t .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,y)

valPerformance = perform(net,valTargets,y)

testPerformance = perform(net,testTargets,y)% View the Network

view(net)% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)

%figure, ploterrhist(e)

%figure, plotregression(t,y)

%figure, plotfit(net,x,t)

end

# ANSWER

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you can use :

`trainFcn = 'trainlm';`

hiddenLayerSize = 23;

numberhiddenlayers=2;%more hidden layers

net = fitnet([hiddenLayerSize numberhiddenlayers],trainFcn);

net.trainParam.epochs=2000;% more epochs

view(net)

with your code:

`% Solve an Input-Output Fitting problem with a Neural Network`

% Script generated by Neural Fitting app

% Created 13-Sep-2017 20:47:36

%

% This script assumes these variables are defined:

%

% Input_train - input data.

% Target_train - target data.

x = Input_train;

t = Target_train;

% 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'; % Levenberg-Marquardt backpropagation.

% Create a Fitting Network

hiddenLayerSize = 23;

numberhiddenlayers=2; %more hidden layers

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