VGP update: add comments #54
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| S_v = tf.linalg.triangular_solve(Lnn[None], tf.linalg.matrix_transpose(tmp)) # [L, N, N] | ||
| new_q_sqrt = tf.linalg.cholesky(S_v + tf.eye(num_data, dtype=Knn.dtype) * jitter) # [L, N, N] | ||
| new_q_sqrt = tf.linalg.cholesky(S_v + jitter_mat) # [L, N, N] |
There was a problem hiding this comment.
i'm trying to get some context: should this code be available in gpflow instead, since this is trying to be generic anyway?
There was a problem hiding this comment.
I think that there is a function already but couldn't get it to work - besides it's not used everywhere in gpflow
There was a problem hiding this comment.
which function is that?
vpicheny
approved these changes
Nov 20, 2020
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Also, not sure if it's as intended, but if you plan at some point to wrap model predictions in tf.function() as well to increase the runtime speed/efficiency, this will break for (S)GPR - for that, you would have to replace X and Y with tf.Variables() which you .assign() instead of overwriting. Happy to contribute that as a PR if you think it's useful.