Hi,
The error message indicates that the input to the dlgradient must be a scalar value, but Y in your code seems to be a loaded neural network. To use dlgradient, you need to perform a forward pass through the neural network and obtain a scalar output that you want to differentiate with respect to the inputs.
function [Y, dydA, dydB, dydC, dydD] = Y_partial(A, B, C, D)
net = load('i_Y.mat','net'); % Loading the saved network
Y = predict(net.net, A, B, C, D); % Performing a forward pass
[dydA, dydB, dydC, dydD] = dlgradient(Y, A, B, C, D);
end
A = dlarray(A);
B = dlarray(B);
C = dlarray(C);
D = dlarray(D);
[Y, dydA, dydB, dydC, dydD] = dlfeval(@Y_partial, A, B, C, D);
Here, predict performs a forward pass through the neural network to obtain the scalar output Y, which can then be used as the first argument to dlgradient. Y_partial is modified to take the input values as arguments and perform the necessary computation to produce Y and the gradients. After this, dlfeval is used to evaluate Y_partial with the input arguments specified as dlarray objects.
Thanks
