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.
If validation loss == training loss perfect fit
Following are the three cases of model fit:
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
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
- 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