Trying to create a Bayesian neural network using DenseVariational layer in Tensorflow probability. My question is when we set the parameter kl_use_exact to False does by that we are not taking the prior function into consideration. I tried to look at the source code of the DenseVariational class (_make_kl_divergence_penalty function) and I'm more confused then before, I didn't get the use of kl_use_exact.
The parameter kl_use_exact in DenseVariational layer of TF
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kl_use_exactspecifies the calculation method of KL-Divergence between variational posterior and prior.False: KL will be approximated.True: Uses the actual KL value. However it can be setTrueif and only if the KL-Divergence between the distributions are registered in TensorFlow-Probability.