avalanche.training.SynapticIntelligence

class avalanche.training.SynapticIntelligence(*, model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion: ~torch.nn.modules.module.Module | ~typing.Callable[[~torch.Tensor, ~torch.Tensor], ~torch.Tensor], si_lambda: float | ~typing.Sequence[float], eps: float = 1e-07, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.Sequence[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, **base_kwargs)

Synaptic Intelligence strategy.

This is the Synaptic Intelligence PyTorch implementation of the algorithm described in the paper “Continuous Learning in Single-Incremental-Task Scenarios” (https://arxiv.org/abs/1806.08568)

The original implementation has been proposed in the paper “Continual Learning Through Synaptic Intelligence” (https://arxiv.org/abs/1703.04200).

The Synaptic Intelligence regularization can also be used in a different strategy by applying the SynapticIntelligencePlugin plugin.

__init__(*, model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion: ~torch.nn.modules.module.Module | ~typing.Callable[[~torch.Tensor, ~torch.Tensor], ~torch.Tensor], si_lambda: float | ~typing.Sequence[float], eps: float = 1e-07, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.Sequence[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, **base_kwargs)

Init.

Creates an instance of the Synaptic Intelligence strategy.

Parameters:

• model – PyTorch model.

• optimizer – PyTorch optimizer.

• criterion – loss function.

• si_lambda – Synaptic Intelligence lambda term. If list, one lambda for each experience. If the list has less elements than the number of experiences, last lambda will be used for the remaining experiences.

• eps – Synaptic Intelligence damping parameter.

• train_mb_size – mini-batch size for training.

• train_epochs – number of training epochs.

• eval_mb_size – mini-batch size for eval.

• device – PyTorch device to run the model.

• plugins – (optional) list of StrategyPlugins.

• evaluator – (optional) instance of EvaluationPlugin for logging and metric computations.

• eval_every – the frequency of the calls to eval inside the training loop. -1 disables the evaluation. 0 means eval is called only at the end of the learning experience. Values >0 mean that eval is called every eval_every epochs and at the end of the learning experience.

• base_kwargs – any additional BaseTemplate constructor arguments.

Methods

__init__(*, model, optimizer, criterion, ...)	Init.
backward()	Run the backward pass.
check_model_and_optimizer([...])
criterion()	Loss function for supervised problems.
eval(exp_list, **kwargs)	Evaluate the current model on a series of experiences and returns the last recorded value for each metric.
eval_dataset_adaptation(**kwargs)	Initialize self.adapted_dataset.
eval_epoch(**kwargs)	Evaluation loop over the current self.dataloader.
forward()	Compute the model's output given the current mini-batch.
make_eval_dataloader([num_workers, shuffle, ...])	Initializes the eval data loader. :param num_workers: How many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0). :param pin_memory: If True, the data loader will copy Tensors into CUDA pinned memory before returning them. Defaults to True. :param kwargs: :return:.
make_optimizer([reset_optimizer_state, ...])	Optimizer initialization.
make_train_dataloader([num_workers, ...])	Data loader initialization.
model_adaptation([model])	Adapts the model to the current data.
optimizer_step()	Execute the optimizer step (weights update).
stop_training()	Signals to stop training at the next iteration.
train(experiences[, eval_streams])	Training loop.
train_dataset_adaptation(**kwargs)	Initialize self.adapted_dataset.
training_epoch(**kwargs)	Training epoch.

Attributes

is_eval	True if the strategy is in evaluation mode.
mb_task_id	Current mini-batch task labels.
mb_x	Current mini-batch input.
mb_y	Current mini-batch target.
mbatch	Current mini-batch.
mb_output	Model's output computed on the current mini-batch.
dataloader	Dataloader.
optimizer	PyTorch optimizer.
loss	Loss of the current mini-batch.
train_epochs	Number of training epochs.
train_mb_size	Training mini-batch size.
eval_mb_size	Eval mini-batch size.
retain_graph	Retain graph when calling loss.backward().
evaluator	EvaluationPlugin used for logging and metric computations.
clock	Incremental counters for strategy events.
adapted_dataset	Data used to train.
model	PyTorch model.
device	PyTorch device where the model will be allocated.
plugins	List of `SupervisedPlugin`s. .
experience	Current experience.
is_training	True if the strategy is in training mode.
current_eval_stream	Current evaluation stream.