mxnet.lr_scheduler.PolyScheduler¶

class mxnet.lr_scheduler.PolyScheduler(max_update, base_lr=0.01, pwr=2, final_lr=0, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]¶

Reduce the learning rate according to a polynomial of given power.

Calculate the new learning rate, after warmup if any, by:

final_lr + (start_lr - final_lr) * (1-nup/max_nup)^pwr
if nup < max_nup, 0 otherwise.

Parameters

max_update (int) – maximum number of updates before the decay reaches final learning rate.
base_lr (float) – base learning rate to start from
pwr (int) – power of the decay term as a function of the current number of updates.
final_lr (float) – final learning rate after all steps
warmup_steps (int) – number of warmup steps used before this scheduler starts decay
warmup_begin_lr (float) – if using warmup, the learning rate from which it starts warming up
warmup_mode (string) – warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps

__init__(max_update, base_lr=0.01, pwr=2, final_lr=0, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(max_update[, base_lr, pwr, …])	Initialize self.
`get_warmup_lr`(num_update)