Let's say I have a trained model for some dataset X of unknown distribution. How can I use the Glow library to compute the probability of a new data point from the distribution?
I am looking at theinfer()function in train.py and f_encode()in model.py
What I'm doing right now is using the calculated objective in f_encode to compute p = tf.exp(objective). Is this correct? Could someone point me in the right direction?
Let's say I have a trained model for some dataset X of unknown distribution. How can I use the Glow library to compute the probability of a new data point from the distribution?
I am looking at the
infer()
function in train.py andf_encode()
in model.pyWhat I'm doing right now is using the calculated
objective
inf_encode
to computep = tf.exp(objective)
. Is this correct? Could someone point me in the right direction?