I'm in the process of optimizing my neural network. I'd like to optimize on a small training set (1000 rows) as opposed to my full training set (100K rows) for speed reasons.
Will the optimal hyper-parameters (i.e. my learning rate, dropout prob, regularization parameter, # of hidden units, etc...) for my small training set also be optimal for my large training set? In other words, which parameters can I optimize on my small training set, and which must I try to optimize on my large?