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Hi, @andrewsilva9 is correct in line 133 it loops over the entire training dataset so that one grad_z is calculated per training sample.
With the start argument you can start at a different point in the training dataset. This can be used if you split the calculation across multiple machines. You calculate samples [0-100] on machine 1 and on machine two you pass start=101 to calculate from training sample [101-x]. The end x is missing here in the implementation still...
Hi,
I am not sure if I am misunderstanding the parameter or if it shouldn't be passed to
calc_grad_z
:pytorch_influence_functions/pytorch_influence_functions/calc_influence_function.py
Line 111 in 4df5d2e
I assumed it should loop over the whole training set.
Thanks and best regards
Verena
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