jaxopt.perturbations.make_perturbed_fun

jaxopt.perturbations.make_perturbed_fun(fun, num_samples=1000, sigma=0.1, noise=<jaxopt._src.perturbations.Gumbel object>)[source]

Transforms a function into a differentiable version with perturbations.

Parameters

fun (Callable[[Array], float]) – the function to transform into a differentiable version. Signature for fun currently supported for custom jvp and jit is: + input [D1, …, Dk], output [R_1, …, R_r]
num_samples (int) – an int, the number of perturbed outputs to average over.
sigma (float) – a float, the scale of the random perturbation.
noise – a distribution object that must implement a sample function and a log-pdf of the desired distribution, similar to the examples above. Default is Gumbel distribution.

Returns

A function with the same signature (and an rng) that can be differentiated.

Example

Given an argmax function such as:

def fun(x):
  return jax.nn.relu(x)

pert_fun = make_perturbed_fun(fun,
                              num_samples=200,
                              sigma=0.01)

Then pert_fun is differentiable, a perturbed version of fun. Since it relies on randomness, it requires an rng key:

pert_output = pert_fun(x, rng)

When handling a batched input, vmap can be applied to this function, with some care in splitting the rng key:

batch_size = x_batch.shape[0]
rngs_batch = jax.random.split(rng, batch_size)
pert_batch = jax.vmap(pert_fun)(x_batch, rngs_batch)

Further, if fun is jittable, then so is pert_fun.