Sorry for the late reply, I have to go through once on Convolution Layers. So, in my case, I choose 1000 images from each class and 1000 epochs to train. So, I can do overfitting on this data. According to you, In many cases when we see that a model is fitting well, but there might be some fault that we can not see or just ignore, but can cause tremendous failure in the actual result, so overfitting can make sure that the dataset can properly fit on unseen data, right?
Also, I have never used learning_weight_decay, but studied it in theory, can you kindly show me some code, about how to do it with example? Also after how can I test on a real dataset? It has never trained on the whole dataset? Do you mean by I have to train 1000 images each time and train on 10 times as I have nearly 10000 images per class?
If it trains on a 1000 images, then for the rest 9000 images what should I do? Thanks in advance.