nntoolbox.callbacks.callbacks module

class nntoolbox.callbacks.callbacks.Callback[source]

Bases: object

after_backward() → bool[source]
after_losses(losses: Dict[str, torch.Tensor], train: bool) → Dict[str, torch.Tensor][source]
after_outputs(outputs: Dict[str, torch.Tensor], train: bool) → Dict[str, torch.Tensor][source]
after_step() → bool[source]
on_backward_begin() → bool[source]
on_batch_begin(data: Dict[str, torch.Tensor], train) → Dict[str, torch.Tensor][source]
on_batch_end(logs: Dict[str, Any])[source]
on_epoch_begin()[source]
on_epoch_end(logs: Dict[str, Any]) → bool[source]
on_train_begin()[source]
on_train_end()[source]
order: int = 0
class nntoolbox.callbacks.callbacks.CallbackHandler(learner, n_epoch: int, callbacks: Optional[List[nntoolbox.callbacks.callbacks.Callback]] = None, metrics: Optional[Dict[str, nntoolbox.metrics.metrics.Metric]] = None, final_metric: str = 'accuracy')[source]

Bases: object

after_backward() → bool[source]
after_losses(losses: Dict[str, torch.Tensor], train) → Dict[str, torch.Tensor][source]
after_outputs(outputs: Dict[str, torch.Tensor], train) → Dict[str, torch.Tensor][source]
after_step() → bool[source]
on_backward_begin()[source]
on_batch_begin(data: Dict[str, torch.Tensor], train: bool) → Dict[str, torch.Tensor][source]
on_batch_end(logs: Dict[str, Any])[source]
on_epoch_begin()[source]
on_epoch_end(logs: Dict[str, Any]) → bool[source]
on_train_begin()[source]
on_train_end() → float[source]
class nntoolbox.callbacks.callbacks.EarlyStoppingCB(monitor='loss', min_delta: int = 0, patience: int = 0, mode: str = 'min', baseline: Optional[float] = None)[source]

Bases: nntoolbox.callbacks.callbacks.Callback

on_epoch_end(logs: Dict[str, Any]) → bool[source]
class nntoolbox.callbacks.callbacks.GroupCallback(callbacks: List[nntoolbox.callbacks.callbacks.Callback])[source]

Bases: nntoolbox.callbacks.callbacks.Callback

Group several callbacks together (UNTESTED)

after_backward() → bool[source]
after_losses(losses: Dict[str, torch.Tensor], train: bool) → Dict[str, torch.Tensor][source]
after_outputs(outputs: Dict[str, torch.Tensor], train: bool) → Dict[str, torch.Tensor][source]
after_step() → bool[source]
on_backward_begin() → bool[source]
on_batch_begin(data: Dict[str, torch.Tensor], train) → Dict[str, torch.Tensor][source]
on_batch_end(logs: Dict[str, Any])[source]
on_epoch_begin()[source]
on_epoch_end(logs: Dict[str, Any]) → bool[source]
on_train_begin()[source]
on_train_end()[source]