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Why network overfits too early?

I want to train a neural network model, which basicly does binary classification. I can't understand why my network overfits too early. I thought my network is too big and it memorizes the dataset, but when I make it smaller, it does not learn at all. How avoid this situation? dropout didn't work, augmentation techniques helped a bit, obviously regularizations didn't change anything. Can you guys explain the reasons, and how I can avoid it?

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1 Answers

Have you tried early stopping ?


I have not tried it, because it takes 3-4 epoches and the networks starts to overfit. I thought learning rate is too big or small. changed them, it didn't work as well.

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