def __init__(self,
model: Module,
optimizer: Optimizer,
criterion,
ewc_lambda: float,
mode: str = 'separate',
decay_factor: Optional[float] = None,
keep_importance_data: bool = False,
train_mb_size: int = 1,
train_epochs: int = 1,
eval_mb_size: int = None,
device=None,
plugins: Optional[List[StrategyPlugin]] = None,
evaluator: EvaluationPlugin = default_logger):
""" Elastic Weight Consolidation (EWC) strategy.
See EWC plugin for details.
This strategy does not use task identities.
:param model: The model.
:param optimizer: The optimizer to use.
:param criterion: The loss criterion to use.
:param ewc_lambda: hyperparameter to weigh the penalty inside the total
loss. The larger the lambda, the larger the regularization.
:param mode: `standard` to keep a separate penalty for each previous
experience. `onlinesum` to keep a single penalty summed over all
previous tasks. `onlineweightedsum` to keep a single penalty
summed with a decay factor over all previous tasks.
:param decay_factor: used only if mode is `onlineweightedsum`.
It specify the decay term of the importance matrix.
:param keep_importance_data: if True, keep in memory both parameter
values and importances for all previous task, for all modes.
If False, keep only last parameter values and importances.
If mode is `separate`, the value of `keep_importance_data` is
set to be True.
:param train_mb_size: The train minibatch size. Defaults to 1.
:param train_epochs: The number of training epochs. Defaults to 1.
:param eval_mb_size: The eval minibatch size. Defaults to 1.
:param device: The device to use. Defaults to None (cpu).
:param plugins: Plugins to be added. Defaults to None.
:param evaluator: (optional) instance of EvaluationPlugin for logging
and metric computations.
"""
ewc = EWCPlugin(ewc_lambda, mode, decay_factor, keep_importance_data)
if plugins is None:
plugins = [ewc]
else:
plugins.append(ewc)
super().__init__(model,
optimizer,
criterion,
train_mb_size=train_mb_size,
train_epochs=train_epochs,
eval_mb_size=eval_mb_size,
device=device,
plugins=plugins,
evaluator=evaluator)
def test_integration(self):
modules = [(SimpleMLP(input_size=6), "classifier")]
for m, name in modules:
self._test_integration(copy.deepcopy(m), name)
self._test_integration(copy.deepcopy(m),
name,
plugins=[LwFPlugin()])
self._test_integration(copy.deepcopy(m),
name,
plugins=[EWCPlugin(0.5)])
def __init__(self,
model: Module,
optimizer: Optimizer,
criterion,
ewc_lambda: float,
mode: str = "separate",
decay_factor: Optional[float] = None,
keep_importance_data: bool = False,
train_mb_size: int = 1,
train_epochs: int = 1,
eval_mb_size: int = None,
device=None,
plugins: Optional[List[SupervisedPlugin]] = None,
evaluator: EvaluationPlugin = default_evaluator,
eval_every=-1,
**base_kwargs):
"""Init.
:param model: The model.
:param optimizer: The optimizer to use.
:param criterion: The loss criterion to use.
:param ewc_lambda: hyperparameter to weigh the penalty inside the total
loss. The larger the lambda, the larger the regularization.
:param mode: `separate` to keep a separate penalty for each previous
experience. `onlinesum` to keep a single penalty summed over all
previous tasks. `onlineweightedsum` to keep a single penalty
summed with a decay factor over all previous tasks.
:param decay_factor: used only if mode is `onlineweightedsum`.
It specify the decay term of the importance matrix.
:param keep_importance_data: if True, keep in memory both parameter
values and importances for all previous task, for all modes.
If False, keep only last parameter values and importances.
If mode is `separate`, the value of `keep_importance_data` is
set to be True.
:param train_mb_size: The train minibatch size. Defaults to 1.
:param train_epochs: The number of training epochs. Defaults to 1.
:param eval_mb_size: The eval minibatch size. Defaults to 1.
:param device: The device to use. Defaults to None (cpu).
:param plugins: Plugins to be added. Defaults to None.
:param evaluator: (optional) instance of EvaluationPlugin for logging
and metric computations.
:param 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.
:param **base_kwargs: any additional
:class:`~avalanche.training.BaseTemplate` constructor arguments.
"""
ewc = EWCPlugin(ewc_lambda, mode, decay_factor, keep_importance_data)
if plugins is None:
plugins = [ewc]
else:
plugins.append(ewc)
super().__init__(model,
optimizer,
criterion,
train_mb_size=train_mb_size,
train_epochs=train_epochs,
eval_mb_size=eval_mb_size,
device=device,
plugins=plugins,
evaluator=evaluator,
eval_every=eval_every,
**base_kwargs)