Try making these 2 modifications:
1. Use "mean(meanAbsoluteError, 'all')" to calculate the MAE. This simplifies the calculation and ensures that the loss is aggregated across all dimensions, resulting in a scalar value. This change is crucial because the training function expects a single scalar loss value to optimize, not an array of losses.
2. In custom layers, especially for regression tasks, both forwardLoss and backwardLoss methods need to be properly defined. Add "backwardLoss" method which calculates the gradient of the loss with respect to the predictions, which is essential for backpropagation during training.
classdef maeRegressionLayer < nnet.layer.RegressionLayer ...
& nnet.layer.Acceleratable
% Example custom regression layer with mean-absolute-error loss.
methods
function layer = maeRegressionLayer(name)
% layer = maeRegressionLayer(name) creates a
% mean-absolute-error regression layer and specifies the layer
% name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = 'Mean absolute error';
end
function loss = forwardLoss(~, Y, T)
% Calculate MAE.
meanAbsoluteError = abs(Y-T);
% Take mean over all elements.
loss = mean(meanAbsoluteError, 'all');
end
function dLdY = backwardLoss(~, Y, T)
% dLdY = backwardLoss(layer, Y, T) returns the derivatives of the
% MAE loss with respect to the predictions Y.
% Calculate gradients
R = size(Y,3);
N = size(Y,4);
dLdY = sign(Y - T) / (R * N);
end
end
end
Hope this helps!
