Comments (5)
Hi, Daniil!
It's absolutely the same construction. Besides clipping T.exp(log_alpha) * self.W * self.W
can be replaced with T.exp(self.log_sigma2 - T.log(self.W ** 2)) * self.W * self.W
that is exatly log_sigma_2
.
The beautiful thing about ART is that you have to change the only parametrization while evaluation is the same.
from variational-dropout-sparsifies-dnn.
Yep, It's the same thing, but if we will look at this equation without local reparameterization we will see that in current implementation you have implied_weight = W + W^2 * alpha * eps
and thus variance of gradient will remain the same
from variational-dropout-sparsifies-dnn.
Actually no. Let's move to math to see it clearly. We are interested in d implied_weight / d log_sigma2
:
implied_weight = W + W^2 * alpha * eps
implied_weight = w + w^2 * exp(log_sigma2) * 1/exp(T.log(self.W ** 2)) * eps
implied_weight = w + exp(log_sigma2) * eps
After several lines, we can see d implied_weight / d log_sigma2
does not depend on alpha that's why thought "additive reparameterization takes no effect in the case of big alpha" is wrong.
from variational-dropout-sparsifies-dnn.
Yep, you are absolutely right. I forgot that alpha is obtained from sigma
and W
.
Thank you for help!
from variational-dropout-sparsifies-dnn.
Wellcome!
from variational-dropout-sparsifies-dnn.
Related Issues (12)
- Will you make the code for the algorithm itself available?
- Details for reproducing LeNet-5 results in ICML paper HOT 1
- Cross entropy loss mutliplied by training set size
- How to run examples on CPU?
- cudnn version? HOT 2
- failed to run on CPU HOT 2
- How to approximate the D_KL? HOT 4
- Блокнот и пример
- How to generate the dynamic image that shows the sparsity of weights through out the training as shown in README HOT 1
- Is it possible to compress already existing trained network? HOT 1
- How do you choose the threshold? HOT 2
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from variational-dropout-sparsifies-dnn.