categorical prior loss
patrickltobing opened this issue · 1 comments
patrickltobing commented
Hi, thank you for the great solution.
Though, regarding the prior loss term,
I think the number of mixtures K
should be included in labeled_loss(x, px_logit, ...):
,
https://github.com/RuiShu/vae-clustering/blob/master/shared_subgraphs.py#L31,
so it will return xy_loss + np.log(K)
.
sghalebikesabi commented
He correctly writes xy_loss - np.log(0.1) = xy_loss - np.log(1/K) = xy_loss + np.log(K)