Hello Aditya!



 kendreaditya:

Results:
The training set can achieve an accuracy of 100% with enough iteration, but at the cost of the testing set accuracy. After around 20-50 epochs of testing, the model starts to overfit to the training set and the test set accuracy starts to decrease (same with loss).


The graphs you posted of your results look fishy.
Your test-set accuracy becomes significantly better than that of
your training set (before falling off again).  While not mathematically
impossible, this is quite unlikely if your test and training set are both
representative of the same “universe” of data.
Also, your test-set loss falls steadily (similarly to, although not a low
as that of your training set).  Although not the same, loss and
accuracy are related, so it’s surprising that your accuracy gets much
better, and then worse again, even as your loss is consistently falling.
(Again, not mathematically impossible, but unlikely.)
Good luck.
K. Frank