artist.optim.training

Classes

EarlyStopping

Initialize the early stopping.

Functions

`exponential`(→ torch.optim.lr_scheduler.LRScheduler)	Create an exponential learning rate scheduler.
`cyclic`(→ torch.optim.lr_scheduler.LRScheduler)	Create a cyclic learning rate scheduler.
`reduce_on_plateau`(→ torch.optim.lr_scheduler.LRScheduler)	Create learning rate scheduler that reduces on plateaus.

Module Contents

artist.optim.training.exponential(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) → torch.optim.lr_scheduler.LRScheduler

Create an exponential learning rate scheduler.

Parameters

optimizerOptimizer: The optimizer.
parametersdict[str, float]: The scheduler parameters.

Returns

LRScheduler: An exponential learning rate scheduler.

artist.optim.training.cyclic(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) → torch.optim.lr_scheduler.LRScheduler

Create a cyclic learning rate scheduler.

Parameters

optimizerOptimizer: The optimizer.
parametersdict[str, float]: The scheduler parameters.

Returns

LRScheduler: A cyclic learning rate scheduler.

artist.optim.training.reduce_on_plateau(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) → torch.optim.lr_scheduler.LRScheduler

Create learning rate scheduler that reduces on plateaus.

Parameters

optimizerOptimizer: The optimizer.
parametersdict[str, float]: The scheduler parameters.

Returns

LRScheduler: A learning rate scheduler that reduces on plateaus.

class artist.optim.training.EarlyStopping(window_size: int = 10, patience: int = 20, min_improvement: float = 0.0001, relative: bool = True, eps: float = 1e-08)

Initialize the early stopping.

Parameters

window_sizeint: Number of epochs used to estimate loss trend (default is 10).
patienceint: Number of consecutive non-improving windows before stopping (default is 20).
min_improvementfloat: Minimum required improvement over the window to reset patience (default is 1e-4).
relativebool: Indicates whether improvement is normalized by loss magnitude (default is True).
epsfloat: Small value for stability (default is 1e-8).

window_size = 10

patience = 20

min_improvement = 0.0001

relative = True

eps = 1e-08

loss_history: Deque[float]

counter = 0

step(loss: float) → bool

Update stopping state.

Parameters

lossfloat: Current loss value.

Returns

bool: True if optimization should stop, otherwise False.