You can use the 'net' object i.e your trained neural network to predict the outputs for the additional test data 'inputtest' and compare it with the expected output 'outputts'. The code is as follows:
% Evaluate the Network on Additional Test Set
yTest = net(inputtest); % Compute the network's output on the additional test set
eTest = gsubtract(outputts, yTest); % Compute the error between the actual and predicted outputs
% Compute Performance on the Additional Test Set
% Using the same performance function used during training (e.g., mean squared error)
performanceTest = perform(net, outputts, yTest);
% Display the performance
fprintf('Performance on the additional test set: %f\n', performanceTest);
Refer to the following documentation link to read about the process of 'Testing Network' using code:
