Nan loss after few epochs about unsupervised-aspect-extraction HOT 5 OPEN

Hi,

I am not sure why you got the NaN loss, since I didn't encounter this issue with the current code or the code before changing batches_per_epoch to 1000. So far, no other people has reported this issue. Maybe you use another machine to run?

As for evaluation, did you manually assign the cluster_map by yourself in evaluation.py? As the cluster_map I provided in the evaluation.py is only used for the uploaded trained model. If you train a model again, you need to manually assign the mapping first before evaluation.

You can try to evaluate the uploaded trained restaurant model by running evaluation.py directly. This will give you similar results as reported in paper. And you can have a look at how I assigned the aspect label to each cluster by looking at the cluster_map and the aspect.log in pre_trained_model/restaurant/.

from unsupervised-aspect-extraction.

from unsupervised-aspect-extraction.

I I had the same problem. since I used a large data（3million). Have you solved the problem yet?

from unsupervised-aspect-extraction.

I am also getting nan values

Aspect 0:
welsh:nan staff:nan bitches:nan sick":nan stolen:nan christmas:nan edward:nan genius:nan selena:nan emily:nan socks:nan 21st:nan kings:nan roof:nan incredibly:nan walmart:nan bein:nan ga:nan luckily:nan gud:nan cricket:nan reunion:nan accidentally:nan kobe:nan steak:nan fridays:nan disneyland:nan snap:nan involved:nan carry:nan security:nan delivery:nan police:nan theatre:nan prince:nan iranelection:nan sounded:nan

Aspect 1:
welsh:nan staff:nan bitches:nan sick":nan stolen:nan christmas:nan edward:nan genius:nan selena:nan emily:nan socks:nan 21st:nan kings:nan roof:nan incredibly:nan walmart:nan bein:nan ga:nan luckily:nan gud:nan cricket:nan reunion:nan accidentally:nan kobe:nan steak:nan fridays:nan disneyland:nan snap:nan involved:nan carry:nan security:nan delivery:nan police:nan theatre:nan prince:nan iranelection:nan sounded:nan

and so on....

from unsupervised-aspect-extraction.

I encountered this problem for some data. Actually, vector representation of aspects is exactly the same. Thus, ortho_reg loss is nan.
Here there is an edited version of the original code with some fixes and modification, I tried, it worked

from unsupervised-aspect-extraction.