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Variational Autoencoders - Week 10
I implemented another definition of the Forward()
function in the FFN class along with a test. This now makes it very easy to forward pass just through the encoder or the decoder of a saved model.
The convolutional model was not working with the Sequential
object because in its Forward()
function, reset
was being hard set to true
. Hence, it was setting the inputWidth
and inputHeight
of the TransposedConvolutional
layer both to 0. I trained that convolutional model and observed the total loss go much below what it went with a dense layered model, as expected. Although, the KL divergence was heigher than dense layered model and as a result sampling from the prior didn't generate defined results.
To put this on the models repository, I had to work with some CMake. It was to learn some basics of CMake for this task.
I added BernoulliDistribution
to ann dists. It will be needed for generating binary MNIST. I tried training a model and it did seem to work. I also added support for beta VAEs which was a very simple task.
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