Data set attached.
I have a trained neural network:
% Fit a feedforward neural network to a set of FDM processing data.% Analysis of errors, computationally and visually.%% Input: fdm_trainingdata.m file% Table with columns for:% layer thickness [mm], deposition speed [mm/s], elastic modulus [MPa],% tensile strength [MPa]% Separate arrays that define the 5 layer thicknesses and 5 deposition% speeds%% Output:% surface plots of neural network fit of elastic modulus and strength% errors between predictions of modulus and measured values% quadratic polynomial regression fits of modulus and strengthclear% Read file; variables are 'trainingdata,' 'missing,' 'layerthick,'% 'speed' and 'inputmat'run('fdm_trainingdata.m');inputs = trainingdata(:,1:2)';targets = trainingdata(:,3:4)';%nvar = length(layerthick);% Create a Fitting NetworkhiddenLayerSize = [15 15];net = fitnet(hiddenLayerSize);% Set up Division of Data for Training, Validation, Testingnet.divideParam.trainRatio = 1;net.divideParam.valRatio = 0;net.divideParam.testRatio = 0;% Train the Network[net,tr] = train(net,inputs,targets);% Test the Networkoutputs = net(inputs);errors = gsubtract(outputs,targets);performance = perform(net,targets,outputs)% View the Networkview(net)
I need generate the modulus and strength prediction plots using matrices that are at least 25×26 in size.
How do I generate predictions using the trained network? I need to generate input vectors of (25×25 =) 625 elements so that I can plot the results using surf command.