Plot_top_losses throws IndexError with the training set

train_interp = ClassificationInterpretation.from_learner(learn, ds_type=DatasetType.Train)

plot_top_losses is still hardcoded with valid_ds

Replacing with[idx]
might work for both.

Will fix.

Just one more question @sgugger. resize doesn’t work if tfms=None?

data = (src.transform(tfms=None, size=150, resize_method=ResizeMethod.SQUISH)

It gives error with learn.lr_find():

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 150 and 113 in dimension 2

I want to try on raw_images with no other transformations except resizing.

Do I need to create a get_transforms() with proper values in order to avoid transformations or is there any other way to do just resizing?

It was a bug fixed yesterday actually, so you won’t have the problem in master (or v1.0.43 when it’s released).

Ok, thanks again. :slight_smile: