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sukun1045 avatar sukun1045 commented on July 1, 2024

Hi, the nn.L1Loss is the same as mean absolute error (MAE). For autoencoder, it is not clear in the paper but using MSE or MAE depends on the datasets. For this demo script, we use MSE for UCLA dataset. In general these two losses functions are pretty similar so I don't think there is huge difference between L1 and L2. it is also one line code that you could easily change to the other one.

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zzzzzzzzzzzz999 avatar zzzzzzzzzzzz999 commented on July 1, 2024

Thank you for the answer. And after training you demo script without changing any code, i found
the accuracy rate about 82% in the first few epochs, but in the subsequent training, the loss was reduced, and the accuracy of classification was in a downward trend.When the training is end in the 500th epoch, the accuracy of knn classification was only about 78%. so i want to know if have you meet the same problem, or if i should train more epochs to see the trend?
image

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sukun1045 avatar sukun1045 commented on July 1, 2024

oh you don't have to train 500 epochs. UCLA dataset is pretty small so it converges very fast. If you train too long, the model may be overfitting and the scores shown in the outputs are testing acc, so it is possible to decrease. And since the losses are designed to do the reconstruction task (instead of classification), decreasing loss does not necessary lead to better classification accuracy.

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zzzzzzzzzzzz999 avatar zzzzzzzzzzzz999 commented on July 1, 2024

oh OK thank you very much!!!

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