I am trying to make a Convolutional neural network. Training the images of different brands of Logos. Have 100 images per class and there are 40 classes. I have trained the model now want to check that model is overfitted or not . What should be the number of epochs we will se that with no improvement after which training will be stopped . Should I see after 2, 3 or which number ?
The number of iterations with no improvement is a hyperparameter. Scikit-learn has a default value of 10 in the MLPClassifier but of course you can select one depending on your problem. You can also do a grid search over all your model's hyperparameters.
Another advice I would give is that you could first train the model experimentally for a lot of epochs. If you see it getting stuck for a lot of iterations and then increasing after a halt, then it's better to set a higher value. On the other hand if after a lot of time you don't see it improving you should set the max number of iterations with no improvement to a smaller value.