avish
July 9, 2021, 1:15pm
4
Given a model (inherited from nn.Module), calculate Jacobian of parameters of this model with respect to output vector.
I’m also working on this issue when calculating the influence function.
My current solution is to follow this workaround:
So I write this sample code (which works as a workaround):
import torch
import torch.nn as nn
_input=torch.randn(32,3)
layer = nn.Linear(3,4)
criterion=nn.CrossEntropyLoss()
weight = layer.weight
def func(weight):
del layer.weight
layer.weight=weight
return criterion(layer(_input), torch.zeros(len(_input),dtype=torch.long))
torch.autograd.functional.hessia…