Open mcleish7 opened 1 year ago
Hi,
In the model, the --mem flag should add a computational scratchpad to models, so that that not only is an output optimization, but also an intermediate latent "scratchpad".
Best, Yilun
Thank you for the quick response.
Just to clarify, the scratchpad is not currently implemented for the graph models, so the --mem
flag should have no impact currently?
Thanks, Sean
I believe so -- it should only do something on the MLP models
Thank you so much for the help.
I have another question, if that is okay. The edge weights for the shortest path data set are drawn, independently, from a uniform distribution and then added to their own transpose as the graph is undirected so the matrix should be symmetric. However, in 'Appendix C-Graphical Algorithmic Computation' of the paper for the models, it states edge weights are drawn from a uniform distribution on [0,1], not the the addition of two. Could you confirm which one was used please.
Thanks, Sean
Good Afternoon,
The --mem flag in graph_train.py, does not appear to be used. Only being called: 1) When declaring iterativeFC 2) When declaring GraphEBM
However, it is not used in either constructors: IterativeFC nor EBM.
My testing is showing a large difference in loss when the --mem flag is turned on. What does the flag do please.
Thanks, Sean