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

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 -

- 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.
- 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.

Yes I see

Thank you so much

Okay, I see

Thank you so much