I've trained a model for heart sound classification with transfer learning (MobileNet) on Physionet dataset, and it works fine.

However, when I train it on my own dataset, it seems that it can not learn anything: more specifically, the loss is not decreasing and the accuracy is not going up. I've checked my labels and they seem to be correct. What other things should I check?

  • $\begingroup$ Can you clarify what you mean by "it seems that it cannot learn anything". How did you measure the learning rate? $\endgroup$ Jul 13 at 6:18
  • $\begingroup$ @Jean-MarcVolle it's loss is not decreasing and accuracy is not going up, unfortunately I don't have access to learning curves right now, otherwise I would have shared them. $\endgroup$ Jul 13 at 6:47
  • $\begingroup$ Go through these dot points and try each of them to see if they help: ai.stackexchange.com/a/26987/26726 $\endgroup$
    – Recessive
    Jul 13 at 7:28
  • $\begingroup$ @Recessive I think those points help if it was overfitting, the problem is it not overfitting, it is not learning at all! Accuracy and loss on the training data, itself, does not change! BTW, thanks. $\endgroup$ Jul 13 at 12:11
  • $\begingroup$ @SepehrGolestanian I've had similar issues with training and have had success trying those techniques. Honestly the biggest mistake most people make is not normalizing their data. Without any code or more details though, there is no way for us to help because it could literally be anything that's causing the problem. $\endgroup$
    – Recessive
    Jul 14 at 0:32

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