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I use the files from the "Download Processed Images" (the tar file).
To me it seems that the images become sorted using MiniImageNetDataLoader, and the first images for each label are simpler to classify than later ones?
If I use shuffle = True at the end of the middle line (there are three occurrences of such lines)
random.shuffle(sampled_character_folders)
labels_and_images = self.get_images(sampled_character_folders, range(self.way_num), nb_samples=self.num_samples_per_class, shuffle=False)
labels = [li[0] for li in labels_and_images]
If you use the default libraries of TensorFlow or PyTorch to build the deep neural networks, the order of samples in a single batch will be ignored. So the default setting for shuffle is False. You could set it True. It depends on your code.
If you hope to see an example of how this repo work in a real case, you may see: Meta-Transfer Learning.
I use the files from the "Download Processed Images" (the tar file).
To me it seems that the images become sorted using MiniImageNetDataLoader, and the first images for each label are simpler to classify than later ones?
If I use
shuffle = True
at the end of the middle line (there are three occurrences of such lines)it is much more difficult to train the data.
I got suspicious because the default in
which is overwritten in the call.
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