nerva_torch.learning_rate

Learning-rate schedulers.

These schedulers are intentionally minimal and stateless unless noted (TimeBased updates its internal lr). The parser accepts textual forms such as “Constant(0.1)”, “StepBased(0.1,0.5,10)”, or “MultiStepLR(0.1;1,3,5;0.1)”.

Functions

parse_learning_rate(text)

Parse a textual learning-rate scheduler specification.

Classes

ConstantScheduler(lr)	Constant learning rate: returns the same lr for any epoch.
ExponentialScheduler(lr, change_rate)	Exponential decay: lr * exp(-change_rate * epoch).
LearningRateScheduler()	Interface for epoch-indexed learning-rate schedules.
MultiStepLRScheduler(lr, milestones, gamma)	Multi-step decay: multiply lr by gamma at specified milestone epochs.
StepBasedScheduler(lr, drop_rate, change_rate)	Step decay: lr * drop_rate ^ floor((1+epoch)/change_rate).
TimeBasedScheduler(lr, decay)	Time-based decay: lr = lr / (1 + decay * epoch).

class nerva_torch.learning_rate.LearningRateScheduler

Bases: object

Interface for epoch-indexed learning-rate schedules.

class nerva_torch.learning_rate.ConstantScheduler(lr: float)

Bases: LearningRateScheduler

Constant learning rate: returns the same lr for any epoch.

class nerva_torch.learning_rate.TimeBasedScheduler(lr: float, decay: float)

Bases: LearningRateScheduler

Time-based decay: lr = lr / (1 + decay * epoch).

class nerva_torch.learning_rate.StepBasedScheduler(lr: float, drop_rate: float, change_rate: float)

Bases: LearningRateScheduler

Step decay: lr * drop_rate ^ floor((1+epoch)/change_rate).

class nerva_torch.learning_rate.MultiStepLRScheduler(lr: float, milestones: List[int], gamma: float)

Bases: LearningRateScheduler

Multi-step decay: multiply lr by gamma at specified milestone epochs.

class nerva_torch.learning_rate.ExponentialScheduler(lr: float, change_rate: float)

Bases: LearningRateScheduler

Exponential decay: lr * exp(-change_rate * epoch).

nerva_torch.learning_rate.parse_learning_rate(text: str) → LearningRateScheduler

Parse a textual learning-rate scheduler specification.

Accepted forms include Constant(lr), TimeBased(lr,decay), StepBased(lr,drop_rate,change_rate), MultiStepLR(lr;milestones;gamma) and Exponential(lr,change_rate).