Something I have noticed is that when using ULMFiT, the smaller the batch size I am forced to use the worse my results (regardless of how long I train)
The metrics I am using are accuracy and auroc.
Has anyone seen something similar? I also notice that the loss I get is higher the more I decrease the batch size.
I am using these lines to train the language model and classifier respectively.
learn.fit_one_cycle(10, lr, moms=(0.8,0.7), wd=0.1)
learn.fit_one_cycle(1, lr, moms=(0.8,0.7), wd=0.1)
I use different learning rates and epocs accordingly as well.
Does anyone have any suggestions? Should I be tuning the moms and wd as well?
I’m getting the final results out of the recorder with:
results = dict(zip(learn.recorder.metrics_names, learn.recorder.metrics[-1]))
Also if someone knows a batter way to get what the results were on the validation set I’d love to know.