gradient value is very small in energy constraints situation
Opened this issue · 4 comments
I tried to implement the knowledge network in the energy constraints situation on my own task. However, I found that the grad
from the get_mean_shift
function is very small, which will not have any effect on the original mean. For example, the grad
values are below:
tensor([[[[ 2.7554e-08]],
[[-8.8887e-09]],
[[ 8.4224e-09]],
...,
[[ 5.1067e-12]],
[[ 3.8011e-11]],
[[-2.2646e-12]]],
[[[-5.8177e-09]],
[[ 1.4201e-09]],
[[-2.9926e-10]],
...,
[[ 7.6124e-12]],
...
[[-4.7238e-11]],
[[-5.8617e-11]],
[[ 4.8769e-11]]]], device='cuda:0')
So what is your case?
Thank you for your interest in our work and your question. In our experiments, we have observed the norm of the grad is typically around
We plan to release the code for experiments on N-body MNIST in the near future. Please stay tuned for updates and more details.
So your case is also in the energy constraint task? @gaozhihan
Yes, it is the case of the experiments on N-body MNIST, with alignment of energy conservation.
I see. Thanks for your kind reply. We look forward to your released code regarding the energy constraint to revise our code. Thank you very much.