dadaptation 1.1

Learning Rate Free Learning for Adam, SGD and AdaGrad

D-Adaptation

Learning rate free learning for SGD, AdaGrad and Adam!

Details

The provided Pytorch Optimizer classes can be dropped into your project and used as normal.

• Set the LR parameter to 1.0. This parameter is not ignored, rather, setting it larger to smaller will directly scale up or down the D-adapted learning rate.
• If you encounter divergence early on, try change rho to match a reasonable warmup schedule rate for your problem.
• Use the same learning rate scheduler you would normally use on the problem.
• The Adam variant supports AdamW style weight decay, just set decouple=True. It is not turned on by default, so if you are replacing your adam implementation, make sure you use decoupled if necessary.
• Use the log_every setting to see the learning rate being used (d*lr) and the current D bound.
• Only the AdaGrad version supports sparse gradients.
• The IP variants implement a tighter D bound.

Experimental results

License

See the License file.