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Clarify cycliclr param docs #20880

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Clarify cycliclr param docs #20880

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11 changes: 8 additions & 3 deletions torch/optim/lr_scheduler.py
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Expand Up @@ -498,16 +498,21 @@ class CyclicLR(_LRScheduler):
cycle_momentum (bool): If ``True``, momentum is cycled inversely
to learning rate between 'base_momentum' and 'max_momentum'.
Default: True
base_momentum (float or list): Initial momentum which is the
lower boundary in the cycle for each parameter group.
base_momentum (float or list): Lower momentum boundaries in the cycle
for each parameter group. Note that momentum is cycled inversely
to learning rate; at the peak of a cycle, momentum is
'base_momentum' and learning rate is 'max_lr'.
Default: 0.8
max_momentum (float or list): Upper momentum boundaries in the cycle
for each parameter group. Functionally,
it defines the cycle amplitude (max_momentum - base_momentum).
The momentum at any cycle is the difference of max_momentum
and some scaling of the amplitude; therefore
base_momentum may not actually be reached depending on
scaling function. Default: 0.9
scaling function. Note that momentum is cycled inversely
to learning rate; at the start of a cycle, momentum is 'max_momentum'
and learning rate is 'base_lr'
Default: 0.9
last_epoch (int): The index of the last batch. This parameter is used when
resuming a training job. Since `step()` should be invoked after each
batch instead of after each epoch, this number represents the total
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