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
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