Training Loss and Validation Loss in Deep Learning

Question

Would you please guide me how to interpret the following results?

1) loss < validation_loss 2) loss > validation_loss

It seems that the training loss always s

Answer 1

In machine learning and deep learning there are basically three cases

1) Underfitting

This is the only case where loss > validation_loss, but only slightly, if loss is far higher than validation_loss, please post your code and data so that we can have a look at

2) Overfitting

loss << validation_loss

This means that your model is fitting very nicely the training data but not at all the validation data, in other words it's not generalizing correctly to unseen data

3) Perfect fitting

loss == validation_loss

If both values end up to be roughly the same and also if the values are converging (plot the loss over time) then chances are very high that you are doing it right

Answer 2

Really a fundamental question in machine learning.

If validation loss >> training loss you can call it overfitting.
If validation loss  > training loss you can call it some overfitting.
If validation loss  < training loss you can call it some underfitting.
If validation loss << training loss you can call it underfitting.

Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing. All that matters in the end is: is the validation loss as low as you can get it.

This often occurs when the training loss is quite a bit lower.

Answer 3

1) Your model performs better on the training data than on the unknown validation data. A bit of overfitting is normal, but higher amounts need to be regulated with techniques like dropout to ensure generalization.

2) Your model performs better on the validation data. This can happen when you use augmentation on the training data, making it harder to predict in comparison to the unmodified validation samples. It can also happen when your training loss is calculated as a moving average over 1 epoch, whereas the validation loss is calculated after the learning phase of the same epoch.