Comments (2)
Instead of loss.backward(create_graph=True)
, which is called before hessian_vector_product
use torch.autograd.grad(...)
. I am using Pytorch 1.10.0 and it works. (Also dont forget to filter out unused parameters-grads)
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I can confirm that the workaround suggested by @petrgeiger-incieve seems to be working on my end. I am using PyTorch
1.12
btw.
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Related Issues (18)
- How do we compute eigenvalues of Hessian matrices for each weight matrices or for each module in a model rather than calculating the eigenvalue for the whole model?? HOT 10
- Hi, a question of inconsistency of the dymamics of tr(FIM) and tr(H).
- Where is the hessian trace calculation w.r.t activations? HOT 1
- Computing sum of square of diagonal entries of Hessian
- edits for PINN/custom loss functions
- How to calculate average Hessian trace?
- torch.eig deprecated please use torch.linalg.eig for line 261 of hessian.py HOT 2
- Potential bugs
- No module named 'pyhessian.client'
- deprecation of torch.eig method
- Question about backward() with create_graph=True warning
- A question on the computation of Hessian-vector product HOT 2
- Large variance of results HOT 1
- computational (time) cost HOT 3
- RuntimeError: derivative for grid_sampler_2d_backward is not implemented HOT 1
- Unexpected `shape` issue in Hessian-Vector computation HOT 4
- Hi! Can Pyhessian Perform stage-wise Hessian trace?
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