I am currently writing my thesis on deep learning models where I train a VGG like model. I trained my model always with Early Stopping function from Keras, where it stopped training after approximately 100 Epochs. My professor asked why I stop after 100 Epochs and that it is very few epochs. He said I should also try 500, 700, 800 Epochs and see if my model is overfitting or does a better job. After trying out to train my model on 800 Epochs, this is what comes out:
Looking at Validation Accuracy and Validation Loss values it looks quite good:
Acc: 1.00; Val Acc: 0.9805; Loss: 0.0019; Val Loss 0.00
But the first thing that comes to my mind is: My proffessor always told us that in the real world or a really realistic model should never have more than let's say 94% of accuracy (given it is a little bit more complex task and not just: is this image black or white). Looking at the image I also see there is a lot of noise for Val Loss. Does that mean my model is overfitting or what can I understand from this.
for more information: I used save best model with the parameter Val Loss because its the only parameter that stagnates so much every time. I have 2 classes with around 8000 images. My learning rate is 0.0001, my val split is 0.35 and batch size is 32 (because bigger batch size causes gpu memory error).