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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.
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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. Whether to allow differentiating with respect to integer valued inputs. Here's an example import jax import jax.
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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.
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