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| # %% | |
| #id first_training | |
| #caption Results from the first training | |
| # CLICK ME | |
| #|export | |
| from fastai.vision.all import * | |
| path = untar_data(URLs.PETS)/'images' | |
| def is_cat(x): return x[0].isupper() | |
| dls = ImageDataLoaders.from_name_func( | |
| path, get_image_files(path), | |
| valid_pct=0.2, | |
| seed=42, | |
| label_func=is_cat, | |
| item_tfms=Resize(224) | |
| ) | |
| learn = vision_learner(dls, resnet34, metrics=error_rate) | |
| learn.fine_tune(1) | |
| # %% | |
| from IPython.display import display | |
| from ipywidgets import widgets | |
| uploader = widgets.FileUpload() | |
| display(uploader) | |
| # %% | |
| #|export | |
| # For the book, we can't actually click an upload button, so we fake it | |
| uploader = SimpleNamespace(data = ['data/interim/th-1.webp']) | |
| # %% | |
| #|export | |
| img = PILImage.create(uploader.data[0]) | |
| is_cat,_,probs = learn.predict(img) | |
| print(f"Is this a cat?: {is_cat}.") | |
| print(f"Probability it's a cat: {probs[1].item():.6f}") | |