timm documentation
Learning Rate Schedulers
Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in timm.
Schedulers
Factory functions
timm.scheduler.create_scheduler_v2
< source >( optimizer: Optimizersched: str = 'cosine'num_epochs: int = 300decay_epochs: int = 90decay_milestones: typing.List[int] = (90, 180, 270)cooldown_epochs: int = 0patience_epochs: int = 10decay_rate: float = 0.1min_lr: float = 0warmup_lr: float = 1e-05warmup_epochs: int = 0warmup_prefix: bool = Falsenoise: typing.Union[float, typing.List[float]] = Nonenoise_pct: float = 0.67noise_std: float = 1.0noise_seed: int = 42cycle_mul: float = 1.0cycle_decay: float = 0.1cycle_limit: int = 1k_decay: float = 1.0plateau_mode: str = 'max'step_on_epochs: bool = Trueupdates_per_epoch: int = 0 )
Scheduler Classes
class timm.scheduler.CosineLRScheduler
< source >( optimizer: Optimizert_initial: intlr_min: float = 0.0cycle_mul: float = 1.0cycle_decay: float = 1.0cycle_limit: int = 1warmup_t = 0warmup_lr_init = 0warmup_prefix = Falset_in_epochs = Truenoise_range_t = Nonenoise_pct = 0.67noise_std = 1.0noise_seed = 42k_decay = 1.0initialize = True )
Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983.
Inspiration from https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
k-decay option based on k-decay: A New Method For Learning Rate Schedule - https://arxiv.org/abs/2004.05909
class timm.scheduler.MultiStepLRScheduler
< source >( optimizer: Optimizerdecay_t: typing.List[int]decay_rate: float = 1.0warmup_t = 0warmup_lr_init = 0warmup_prefix = Truet_in_epochs = Truenoise_range_t = Nonenoise_pct = 0.67noise_std = 1.0noise_seed = 42initialize = True )
class timm.scheduler.PlateauLRScheduler
< source >( optimizerdecay_rate = 0.1patience_t = 10verbose = Truethreshold = 0.0001cooldown_t = 0warmup_t = 0warmup_lr_init = 0lr_min = 0mode = 'max'noise_range_t = Nonenoise_type = 'normal'noise_pct = 0.67noise_std = 1.0noise_seed = Noneinitialize = True )
Decay the LR by a factor every time the validation loss plateaus.
class timm.scheduler.PolyLRScheduler
< source >( optimizer: Optimizert_initial: intpower: float = 0.5lr_min: float = 0.0cycle_mul: float = 1.0cycle_decay: float = 1.0cycle_limit: int = 1warmup_t = 0warmup_lr_init = 0warmup_prefix = Falset_in_epochs = Truenoise_range_t = Nonenoise_pct = 0.67noise_std = 1.0noise_seed = 42k_decay = 1.0initialize = True )
Polynomial LR Scheduler w/ warmup, noise, and k-decay
k-decay option based on k-decay: A New Method For Learning Rate Schedule - https://arxiv.org/abs/2004.05909
class timm.scheduler.StepLRScheduler
< source >( optimizer: Optimizerdecay_t: floatdecay_rate: float = 1.0warmup_t = 0warmup_lr_init = 0warmup_prefix = Truet_in_epochs = Truenoise_range_t = Nonenoise_pct = 0.67noise_std = 1.0noise_seed = 42initialize = True )
class timm.scheduler.TanhLRScheduler
< source >( optimizer: Optimizert_initial: intlb: float = -7.0ub: float = 3.0lr_min: float = 0.0cycle_mul: float = 1.0cycle_decay: float = 1.0cycle_limit: int = 1warmup_t = 0warmup_lr_init = 0warmup_prefix = Falset_in_epochs = Truenoise_range_t = Nonenoise_pct = 0.67noise_std = 1.0noise_seed = 42initialize = True )
Hyberbolic-Tangent decay with restarts. This is described in the paper https://arxiv.org/abs/1806.01593