I want to know if it’s possible to modify the parameters of an nn.Module after backpropagation has been done.
I want to clamp the parameters of a module such that they are increasing ie. C < C < … < C[n-1]. But only after they have been updated via gradient descent.
PS. not sure what the category would be so I left it under “Uncategorized”
Yes, similar to how the
optimizer.step() function manipulates the parameters, you could apply a similar logic by e.g. manipulating the parameters inplace in a
Thanks ptrblck for your reply!
I should have been more clear - I want to do it automatically whenever the backwards call is done. I’m developing an nn.module that can be imported and used by just adding it to another module. I don’t want end-users to modify their code so they have to manually clamp the values of my module after the optimizer.step() is called.
I looked into backward hooks but they modify the gradients before the parameters are updated. I want to make something like a “backward_post” hook that executes after the parameter gets updated.
Maybe using a parametrization could be useful in your case. Although a parametrization would modify the parameter before it is used, I think this would achieve the same result. Here is an example of how you could do that:
from torch import nn
import torch.nn.utils.parametrize as parametrize
"""Reparametrize a variable to be bounded in [0, 1] with a clamp"""
def forward(self, X):
return X.clamp(0, 1)
def right_inverse(self, Z):
linear = nn.Linear(1,2)
parametrize.register_parametrization(linear, 'weight', Clamp())
Thanks grudloff! Parametrization solves my problem
I was thinking about using pre_forward_hooks but, if I am not mistaken, those are supposed to be used for modifying the inputs to the module and not the module itself. Parametrization is basically that but for parameters.
I also didn’t know about parameterization and read the tutorial on it after seeing your reply. So thanks for introducing it to me too.