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View Code? Open in Web Editor NEWApproximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch
License: MIT License
Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch
License: MIT License
Thank you for the beautiful implementation!
For the bias term in filter normalize, normalize is applied to filter wise multiple times in your implementation.ใ
I believe the following implementation is correct. What do you think?
def filter_normalize_(self, ref_point: 'ModelParameters', order=2):
"""
In-place filter-wise normalization of the tensor.
:param ref_point: use this model's filter norms, if given
:param order: norm order, e.g. 2 for L2 norm
:return: none
"""
for l in range(len(self.parameters)):
# normalize one-dimensional bias vectors
if len(self.parameters[l].size()) == 1:
self.parameters[l] *= (ref_point.parameters[l].norm(order) / self.parameters[l].norm(order))
else:
# normalize two-dimensional weight vectors
for f in range(len(self.parameters[l])):
self.parameters[l][f] *= ref_point.filter_norm((l, f), order) / (self.filter_norm((l, f), order))
Hi
The paper "[Visualizing the loss landscape of neural nets]" also uses PCA Directions to plot the optimizer path. Which I think is not implemented here. It would be great if its implemented.
Hi,
I think that random noise is gaussian.
rand_u_like(start_point)โ rand_n_like(start_point)
https://github.com/marcellodebernardi/loss-landscapes/blob/master/loss_landscapes/main.py#L133
original code
https://github.com/tomgoldstein/loss-landscape/blob/master/net_plotter.py#L82
Hi,
It there any way to run loss_landscapes.random_plane on GPU? I tried to run it on Google's Colab with GPU but got an error "
RuntimeError: expected backend CUDA and dtype Float but got backend CPU and dtype Float"
For this commit:
8d34610
could you please create a new release here? https://pypi.org/project/loss-landscapes/
Thank you!
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