Comments (1)
Hi @progressforever - Ranger is still sensitive to learning rate. The reason for the 75% at a flat learning rate is to let it explore the larger loss surface and ideally begin to hover around the widest basin.
The drop then lets it go down into the basin and finalize (ideally) on the best generalization minima.
Hope that helps!
from ranger-deep-learning-optimizer.
Related Issues (20)
- Is there a publication of Ranger? HOT 2
- It makes sense to use it on a batch of 1? HOT 3
- Benchmarck Adaptive Scheduling of Stochastic Gradients
- Gradient centralization was updated HOT 4
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- What the "GC operations" mean?
- TypeError in GC operation for Conv layers and FC layers
- Not able to save the model_state_dict. HOT 1
- Does it works well for transformer?
- The results I tested on the cifar10 dataset are as follows. Ranger's results look strange HOT 1
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- This overload of addcmul_ is deprecated: addcmul_(Number value, Tensor tensor1, Tensor tensor2) HOT 5
- How to use ranger in keras? Please help me. HOT 1
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- Loss stuck after 1 epoch HOT 1
- Is adabelief the best optimizer? HOT 7
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from ranger-deep-learning-optimizer.