Hi Moreshud!



 moreshud:

the results show that my model is overfitting.


I wouldn’t say that your model is overfitting (yet).
It is true that your training loss is lower that your validation loss – and
is going down more rapidly.  But to me overfitting means that your
validation loss starts going up (even while the training loss continues to
decrease).  The idea is that your model is fitting the “random” specifics
of your training set so much that it degrades your model’s previous
ability to make good predictions on your validation set.
So keep training for more epochs as long as your validation loss is
still going down – even if it’s larger than your training loss.
Another observation of greater concern to me is that your training
loss starts out significantly larger than your validation loss.  This is
something you should get to the bottom of.  Is it just a statistical fluke?
You could rerun your training with different random partitions of your
data into the training and validation sets.  Are you computing your
training and validation losses in slightly different ways so that you’re
not making a direct comparison?  Is it an outright bug?
Best.
K. Frank