1
$\begingroup$

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?

Thanks--

$\endgroup$
2
$\begingroup$

This is a bad idea. For many tasks, you'll likely get poor performance. For many machine learning tasks, having a lot of data is essential to getting good results.

Instead, I recommend you set yourself up with software and hardware that can train your network on the full training set efficiently: buy a fast GPU, use software that can use the GPU for training, use stochastic gradient descent with mini-batches and other standard techniques.

You'll likely need to optimize all of your hyper-parameters on the full training set. I don't think optimizing the hyper-parameters on a small training set is likely to work well. If there's any past research on similar machine learning tasks, you might look at what network architecture and hyper-parameters they used as use that as a starting point for your exploration.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.