Applying Monte Carlo Dropout in CNN as Bayesian approximation

1

Hi
I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, as I know we apply it during both the training and the test time, and we should multiply the dropout output by 1/(1-p) where p is the dropout rate, could anyone confirm these statements, or give me an example of how should be the code, please.

2

The dropout scaling is already done in the backend, if you are using the nn.Dropout module:

drop = nn.Dropout()
x = torch.ones(10)
out = drop(x)
print(out)
> tensor([0., 0., 2., 2., 0., 0., 0., 2., 0., 0.])

so you don’t need to reapply it.

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