def get_loss(self, forward_pass: ForwardPass, y: Tensor = None) -> Loss:
"""Get a `Loss` for the given forward pass and resulting rewards/labels.
Take a look at the `AuxiliaryTask` class for more info,
NOTE: This is the same simplified version of EWC used throughout the
other examples: the loss is the P-norm between the current weights and
the weights as they were on the begining of the task.
Also note, this particular example doesn't actually use the provided
arguments.
"""
if self.previous_task is None:
# We're in the first task: do nothing.
return Loss(name=self.name)
old_weights: Dict[str, Tensor] = self.previous_model_weights
new_weights: Dict[str, Tensor] = dict(self.model.named_parameters())
loss = 0.0
for weight_name, (new_w,
old_w) in dict_intersection(new_weights,
old_weights):
loss += torch.dist(new_w,
old_w.type_as(new_w),
p=self.options.distance_norm)
ewc_loss = Loss(name=self.name, loss=loss)
return ewc_loss
def get_loss(self, *args, **kwargs) -> Loss:
"""Gets the loss.
NOTE: This is a simplified version of EWC where the loss is the P-norm
between the current weights and the weights as they were on the begining
of the task.
This doesn't actually use any of the provided arguments.
"""
if self.previous_task is None:
# We're in the first task: do nothing.
return Loss(name=self.name)
old_weights: Dict[str, Tensor] = self.previous_model_weights
new_weights: Dict[str, Tensor] = dict(self.model.named_parameters())
loss = 0.
for weight_name, (new_w,
old_w) in dict_intersection(new_weights,
old_weights):
loss += torch.dist(new_w,
old_w.type_as(new_w),
p=self.options.distance_norm)
self._i += 1
ewc_loss = Loss(
name=self.name,
loss=loss,
)
return ewc_loss
def ewc_loss(self) -> Tensor:
"""Gets an 'ewc-like' regularization loss.
NOTE: This is a simplified version of EWC where the loss is the P-norm
between the current weights and the weights as they were on the begining
of the task.
"""
if self._previous_task is None:
# We're in the first task: do nothing.
return 0.0
old_weights: Dict[str, Tensor] = self.previous_model_weights
new_weights: Dict[str, Tensor] = dict(self.named_parameters())
loss = 0.0
for weight_name, (new_w, old_w) in dict_intersection(new_weights, old_weights):
loss += torch.dist(new_w, old_w.type_as(new_w), p=self.reg_p_norm)
return loss