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Define a way of deactivating the effects of the "datamodel" in a RVs which is built
inside the context of a datamodel. This might be the case of RVs
defined inside a Bayesian NN (probabilistic weights or activations). Take for instance
the following code where weights are created when decoder is invoked.
@inf.probmodel
def vae(k, d0, d, decoder):
with inf.datamodel():
z = inf.Normal(tf.ones(k), 1,name="z")
x = inf.Normal(decoder(z, d0, d), 1, name="x")
This might also have some effects on the dependencies graph: we might find the case that
a RV is "deactivated" but it has a parent and a children inside the datamodel. This might be the case of activations.
It might be interesting as well to add deactivation flag in the RV constructor.
The text was updated successfully, but these errors were encountered:
Define a way of deactivating the effects of the "datamodel" in a RVs which is built
inside the context of a datamodel. This might be the case of RVs
defined inside a Bayesian NN (probabilistic weights or activations). Take for instance
the following code where weights are created when
decoder
is invoked.This might also have some effects on the dependencies graph: we might find the case that
a RV is "deactivated" but it has a parent and a children inside the datamodel. This might be the case of activations.
It might be interesting as well to add deactivation flag in the RV constructor.
The text was updated successfully, but these errors were encountered: