I want to use JAX as a vehicle for gradient descent; however, I have a moderately large number of parameters and would prefer to pass them as a dictionary f(func, dict) rather than f(func, x1, …xn). So instead of Something more like Is this possible? EDIT: This is my current work around solution: The gist is that now I don’t
Tag: jax
Why does GPU memory increase when recreating and reassigning a JAX numpy array to the same variable name?
When I recreate and reassign a JAX np array to the same variable name, for some reason the GPU memory nearly doubles the first recreation and then stays stable for subsequent recreations/reassignments. Why does this happen and is this generally expected behavior for JAX arrays? Fully runnable minimal example: https://colab.research.google.com/drive/1piUvyVylRBKm1xb1WsocsSVXJzvn5bdI?usp=sharing. For posterity in case colab goes down: Thank you! Answer
Error when trying to jit the computation of the Jacobian in JAX: “ValueError: Non-hashable static arguments are not supported”
This question is similar to the question here, but I cannot link with what I should alter. I have a function where variational_parameters is a vector (one-dimensional array) of length P, eps is a two-dimensional array of dimensions K by N, and a, b are fixed values. The elbo has been successfully vmapped over the rows of eps, and has
JAX: Getting rid of zero-gradient
Is there a way how to modify this function (MyFunc) so that it gives the same result, but its derivative is not zero gradient? EDIT: Similar function which doesn’t give zero gradient – but it doesn’t return 30/20/10 Answer The gradient of your function is zero because this is the correct result for the gradient as your function is defined.
(Conv1D) Tensorflow and Jax Resulting Different Outputs for The Same Input
I am trying to use conv1d functions to make a transposed convlotion repectively at jax and tensorflow. I read the documentation of both of jax and tensorflow for the con1d_transposed operation but they are resulting with different outputs for the same input. I can not find out what the problem is. And I don’t know which one produces the correct
Not able to install jaxlib
I am trying to install jaxlib on my windows 10 by the following command which I found on the documentation.. pip install jaxlib It shows the following error Answer Jaxlib is not supported on windows you can see it here.. https://github.com/google/jax/issues/438