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W1D3_MultiLayerPerceptrons: Sec 2.1 #722
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@wizofe, thank you for contributing to our repo. Although I do not remember by heart where this is used, shuffling is False during the test as it does not matter the order of the images. While during training, the order matters as we split the dataset into batches. Does this make sense? The |
Hi @wizofe, I agree with Spiros here. I'll elaborate below just to clarify a few things (hopefully).
I hope that helps. Feel free to comment below if we can be of further assistance. :) |
Hi @spirosChv thanks for your comment and @GaganaB for the great insight and explanation. I think I do understand the case of train vs test, although I've previously only seen in practice shuffling both. Is there any advantage to that @GaganaB? |
In practice, there is no advantage to shuffling the test set. During the test phase, the inputs pass through a static network. So, the order does not matter. However, you want to shuffle to have a more unbiased estimation during training. Imagine that you have collected some images; the first half is clean, whereas the second half is blurry. If you do not shuffle, your network will never learn the existence of both. PS. I suggest continuing this conversation on discord to increase visibility from other TAs/Students/etc. Thank you. |
Thank you both! |
drop_last=True
used on train loader but on the testthe same is for shuffle=True vs shuffle=False in both W1D3 notebooks. Incosistency?
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