Thanks Marius, I will try the is_reg parameter.
As for my comment on the weights: my data is strongly unbalanced. So when I was using images as an input, my code below was giving more weights to the classes that were a minority versus the other majority classes:
arch=resnet34
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))
learn = ConvLearner.pretrained(arch, data, precompute=True)
loss_weights = torch.FloatTensor([0.99,0.01]).cuda()
learn.crit = partial(F.cross_entropy, weight=loss_weights)
learn.fit(0.001, 15)
The 2 lines I added are loss_weights and learn.crit, following the discussion in this thread:
I wonder if there is something similar we can use for classification of structured data using classes as output.