I have build a custom Skin cancer classification system using Keras(2.2.2),python(3.6),tensorflow(1.9.0).
Here is the training accuracy,validation accuracy and validation loss graph I am getting (epochs is given in the x axis).
Is it safe to assume after the epoch 640 my model is over fitting ?.
Can we say that the we have reached global minima and just oscillating there ?
It doesn't look it's over fitting because there is not a big difference between the training and validation accuracy. Assuming network has trained fully still it can get stuck in local minima. Try experimenting with different optimizers and change hyperparameters.
But one thing i want to point out is that accuracy is not a good metric to evaluate your model. Check this link for more details: https://stats.stackexchange.com/questions/312780/why-is-accuracy-not-the-best-measure-for-assessing-classification-models
Yes, at 640 training is definitely going wrong. From your graph, you have most likely been on the wrong track since epoch 200. By using future knowledge, you can retrain your your set,and at epoch 200, give your set a Jitter slightly greater than 1/2 the bounce that happens later(around 400 epochs),to prevent falling into local minima, and continue for ~100 - 300 epochs. By adjusting earlier, you give the model a chance to adapt to unknowns.
