# Learning rate Scheduler

A very important aspect in deep learning is the learning rate. Can someone tell me, how to initialize the lr and how to choose the decaying rate. I'm sure there are valuable pointers that some experienced people in the community can share with others. I've noticed that many choose to do a custom scheduler rather than use available ones.

Can someone tell me why and what influences the change in the lr? And when to describe a lr as being small, medium or large? I want to understand it enough to actually make sound choices. Thank you kind souls. I appreciate this community very much.

• As far as I know, it totally depends on the problem and differs from case to case, you can try a set of combinations of learning rates and decays then choose the one that better fits your problem. You can start from a conventional gamut like lr = [1e-6,..., 1e-1] – Fatemeh Asgarinejad Nov 15 '19 at 21:24
• @Fatemeh thank you Fatemeh – user Nov 16 '19 at 9:38