I’ve been struggling with this as well, but there seems to be an easy way to see the full sample. You can increase the trunc_at=150:
For your dataloaders:
dls.show_batch(max_n=10, trunc_at=500)
For your plot_top_losses to see where your models makes the biggest mistakes:
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_top_losses(10, trunc_at=500)
You might need to increase above 500 as well of course, just have a look at the printed result to see if you can read enough data.