Predict output values from trained neural network

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 strength

clear

% 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 Network
    hiddenLayerSize = [15 15];
    net = fitnet(hiddenLayerSize);

% Set up Division of Data for Training, Validation, Testing
    net.divideParam.trainRatio = 1;
    net.divideParam.valRatio = 0;
    net.divideParam.testRatio = 0;
 
% Train the Network
[net,tr] = train(net,inputs,targets);
 
% Test the Network
    outputs = net(inputs);
    errors = gsubtract(outputs,targets);
    performance = perform(net,targets,outputs)
 
% View the Network
    view(net)

I need generate the modulus and strength prediction plots using matrices that are at least 25x26 in size.

How do I generate predictions using the trained network? I need to generate input vectors of (25x25 =) 625 elements so that I can plot the results using surf command.

Answer 1

[Ameer Hamza]

Since your NN have two inputs so suppose you have two input matrices of size 25x25. Copy the following code and add it to the end of your current code.

[X,Y] = meshgrid(linspace(0,1,25), linspace(20,60,25)); % input needs to be meshgrid to be used with surf
out = net([X(:)'; Y(:)']);
out1 = reshape(out(1,:), 25, 25);
out2 = reshape(out(2,:), 25, 25);
figure();
surf(X,Y,out1);
figure();
surf(X,Y,out2);

Since there are two outputs, it will generates two surf plots.

out1:

out2: