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I watch through your code and just confused how you add gaussian distribution over weights at each layer? Because as the paper said, it should add gaussian probability over the weights according to bayesian NN. Or only using dropout is fine (dropout is equal to dropout variational inference?)
Thanks.
The text was updated successfully, but these errors were encountered:
Hi,
I watch through your code and just confused how you add gaussian distribution over weights at each layer? Because as the paper said, it should add gaussian probability over the weights according to bayesian NN. Or only using dropout is fine (dropout is equal to dropout variational inference?)
Thanks.
The text was updated successfully, but these errors were encountered: