Why network overfits too early?

Question

Why network overfits too early?

I want to train a neural network model, which basically 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 them?

Deep LearningComputer Vision

overfitneural-networkclassification

Sunday, March 24, 2019 5:56 PM

4 votesLPLuke Polin262.00

1 Answers

Couldn't find what you were looking for?and we will find an expert to answer.

How helpful was this page?

shiva kumar reddy Kothuru · Accepted Answer

sKshiva kumar reddy Kothuru3.003 years ago

1

Have you tried early stopping ?

Reply

LPLuke Polin262.003 years ago

1

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.