Hi EngM,
In the Deep Learning Toolbox, if the built-in performance metrics like Mean Squared Error (MSE) do not meet your specific needs, you have the option to create a custom performance metric. This involves defining a MATLAB function that accepts the network's predictions, the true targets, and other parameters, which then computes the error according to your custom criteria.
This custom function can then be integrated into the training process of the neural network, allowing you to optimize the network's weights based on your defined error metric. By doing so, you can tailor the learning process to focus on minimizing the error based on your custom performance function.
Here's an example of how you can define a custom performance function:
function error = myMetric(outputs, targets, additionalInputs)
% Calculate the error based on the desired constraint function and return the error value
error = yourCustomConstraintFunction(outputs, targets, additionalInputs);
end
Once you have defined your custom performance function, you can use it in the training process by specifying it as the performance function when creating the neural network:
net = feedforwardnet(hiddenSizes);
net.performFcn = 'myMetric';
By supplying your own error function, you can train the neural network to minimize any constraint or error metric that you define, even if it is not directly calculated from the network outputs or targets.
Hope this helps
