You can use dlgradient and dlfeval to take derivatives with respect to the input or to the parameters
Here's an example:
I'm going to let this function play the role of the neural network. You should use dlnetwork to create a more complex network.
function y = f(x,theta)
y = sum(sin(theta(1)*x+theta(2)));
end
Then I need to scope the computation of the function so that dlfeval knows where to apply auto-diff. I do that by defining a function that evaluates the network and computes the gradient of interest. In this case:
function [y, dy] = fun_and_deriv(x,theta)
y = f(x,theta);
dy = dlgradient(y,theta);
end
Then I can compute the derivative:
x = dlarray(0:10);
theta = dlarray([1 2]);
[y, dy] = dlfeval(@fun_and_deriv,x,theta)
which gives:
y =
1×1 dlarray
-0.9668
dy =
1×2 dlarray
0.6788 -1.1095
