Resnet model question

When I use resnet34 model, what kind of loss function does it do to minimize the error? and what is the optimizer that is being used?

functools.partial(<class ‘torch.optim.adam.Adam’>, betas=(0.9, 0.99))

<fastai.layers.FlattenedLoss at 0x7f0110363748>

It looks like the default optimizer is Adam and the loss is Flattened Loss

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wait, what is Flattened Loss? Is there a video where Jeremy explains it?

I don’t know about any video, but I put a link to the documentation. In this case it will flatten the input then use CrossEntropyLoss.




Ah I see, I didn’t understand the docs :confused:, but you cleared it for me though, so flattened loss means the input will be a flattened previous layer output and then it goes through a loss function, thanks a lot!

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One important thing to note here, is that the loss function used does not depend on the base model used (resnet34 in your case) but rather on the type of y/label given. If it is aclassification problem Cross Entropy is used, if it is a regression problem, some other function like mse is used. If you do not specify a specific label type, fastai will guess the type of y desired and choose the correct function for that. (All of this happens in the .label_from_xxx part of the datablock api.)


sorry my question seems to cause misunderstanding, I do use this for classification, multi-class classification, so it is categorical cross entropy right? thanks for the heads up :grin:

For mulitclass Cat CrossEntropy is correct, for multi label BCEwithLogits should be the default loss func.

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Is there a way to see training loss also instead of only validation loss on each epoch?