Implementing Bayesian CNN

Hi everyone,
I am trying to implement Bayesian CNN, and am searching for people who have worked on this topic, I really need some help (especially tutorials)?
Thank you,

Hi,
whats your problem? have you searched the papers in this field?
there was this repo that I had used a year ago : https://github.com/kumar-shridhar/PyTorch-BayesianCNN
give that a try and it should get you going.
Update :
strongly recommend you to see : Implementing a Bayesian CNN in PyTorch

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Hi,
I found it complicated,I am searching for an approach to implement Bayesian Deep learning, i found two methods either by bayes by backprop or by dropout, I’ve read that Optimising any neural network with dropout is equivalent to a form of approximate Bayesian inference and a network trained with dropout already is a Bayesian neural network,
Could you please confirm these statements if you have any idea ?

Hi, regarding your two statements -

  1. Yes as per Yarin Gal et al https://arxiv.org/abs/1506.02142, you could interpret training a neural net with dropout as conducting Bayesian inference.
  2. A Bayesian neural net is one that has a distribution over it’s parameters. Using dropout allows for the effective weights to appear as if sampled from a weight distribution. If you were to remove the dropout layer, then you’d have point estimates which would no longer correspond to a bayesian network.
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Yes I see
Thank you so much

Okay, I see
Thank you so much