Hello, I need to implement convolutional and partly bayesian neural networks. I looked into Turing.jl but it looks like it still doesn’t provide such option. Any suggestion?
Looking at her code, she only used dense layers…
https://github.com/elizavetasemenova/BNN_tox/blob/master/functions.jl
Forgive my ignorance. Why would it matter? Isn’t Turing still just sending in parameter estimates and recieving the final outputs from the neural net?
Not that I’m doubting you. I’m just genuinely interested!
Yes, maybe you are right. I’m still rather new to Julia…
I was just scared from this description:
The below code is intended for use in more general applications, where you need to be able to change the basic network shape fluidly. The code above is highly rigid, and adapting it for other architectures would be time consuming. Currently the code below only supports networks of
Denselayers.
You can find it here
I’m in the code designing phase and I have to decide which parts I have to implement in which language…
Assuming your CNN architecture doesn’t change dynamically part-way through training, the “rigid” approach shouldn’t be a problem. I’m not aware of limitations that would prevent one from using a convolutional BNN in Julia, so the best approach would be to try for yourself and report any bugs you find 
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