Training Loss and Validation Loss in Deep Learning

Answer-1:

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.

Also check how to prevent overfitting.

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Answer-2:

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

备注：以上答案均来自链接：Training Loss and Validation Loss in Deep Learning - Stack Overflowhttps://stackoverflow.com/questions/48226086/training-loss-and-validation-loss-in-deep-learning