I have a neural network model that outputs a vector Y of size approximately 4000 for about 9 inputs X. I am in need of computing the partial derivative of the output of Y with one or two of the inputs X_1 or X_2.
I already have these derivatives, and I have trained two different neural networks for either of X_1 and X_2. It does quite well, but the issue is that the derivatives are not as accurate as the neural network that computes Y.
I am hoping that there is a way to compute derivatives of the output vector Y to one of the inputs in X from the finalised/optimised neural network, such that I will not need to train two additional neural networks for the derivatives.
Is there a way of doing this with autograd?
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