I have been training a CNN to classify 4 faulty (acoustic emission/250KhZ) signals. I have no problem in implementing the algorithm using tensorflow libraries, but I am confused with which optimizer to choose. I have been trying out different optimizers, for Adam optimizer and the gradient descent optimizer algorithm gave the same results in test classification accuracy(~84% to 85%) on k-fold cross-validation. Results are same for both optimizers, is it because of the data or because of training on huge data(64000 samples/fault condition)?
Are there any specific guidelines to choose a particular optimizer for kind of data? Or else, is it randomly by trial and error?