Partial Derivative Using Autograd

I have a function that takes in a multivariate argument x. Here x = [x1,x2,x3]. Let's say my function looks like: f(x,T) = np.dot(x,T) + np.exp(np.dot(x,T) where T is a constant. I

Solution 1:

I found the following description of the grad function in the autograd source code:

def grad(fun, x)
"Returns a function which computes the gradient of `fun` with
respect to positional argument number `argnum`. The returned
function takes the same arguments as `fun`, but returns the
gradient instead. The function `fun`should be scalar-valued. The
gradient has the same type as the argument."

So

defh(x,t):
    return np.dot(x,t) + np.exp(np.dot(x,t))
h_x = grad(h,0) # derivative with respect to x
h_t = grad(h,1) # derivative with respect to t

Also make sure to use the numpy libaray that comes with autograd

import autograd.numpyas np

instead of

import numpy as np

in order to make use of all numpy functions.