Is there a way to get the category names that the learner is using without going into the data source?
Consider this code:
dls = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=[Resize(224, method='squish')] ).dataloaders(path, bs=32) learn = vision_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(2) // I upload a file... learn.predict(img) // The above outputs: ('electric', tensor(6), tensor([7.5948e-04, 2.1046e-03, 4.5287e-04, 6.6661e-02, 2.0419e-03, 5.7200e-04, 7.8898e-01, 4.6769e-02, 4.2984e-02, 2.3263e-02, 9.3710e-03, 1.7955e-04, 3.1295e-05, 1.5521e-02, 3.0948e-04]))
I’d like to print every possible category with the model predictions in a human readable form, like a category name followed by a percentage of probability but need to know which tensor indexes correspond to each category.
Thanks in advance!