The Shocking Truth About Integer Input Gradient Jax

It appears that you're getting a zero gradient because this is the correct result: Your function has a local gradient of zero at the input values. One way to see this is by. Jax. grad takes an argnums argument that allows for obtaining the gradient of a function with respect to one or more variables, and it returns a tuple of gradients. When you cast to.

Here's an example import jax import jax. Numpy as np jax. Jax is a version of numpy that runs fast on cpu, gpu and tpu, by compiling the computational graph to xla (accelerated linear algebra). It also has an excellent automatic differentiation. Taking gradients with jax. grad. Computing gradients in a linear logistic regression. Differentiating with respect to nested lists, tuples, and dicts. Evaluating a function and its. This happens because odeint's custom gradient rule attempts to compute the gradient wrt all arguments, even arg2 which is an integer and i was not trying to actually. Jax. grad takes a function and returns a new function which computes the gradient of the original function. By default, the gradient is taken with respect to the first argument;

By default, the gradient is taken with respect to the first argument;