(self, state_dict)
Load an optimizer state dict.
        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
Load an optimizer state dict.
        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
Load an optimizer state dict . In general we should prefer the configuration of the existing optimizer instance ( e . g . learning rate ) over that found in the state_dict . This allows us to resume training from a checkpoint using a new set of optimizer args .
def load_state_dict(self, state_dict):
        """Load an optimizer state dict.
        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
        """
        self.optimizer.load_state_dict(state_dict)

        # override learning rate, momentum, etc. with latest values
        for group in self.optimizer.param_groups:
            group.update(self.params)
def load_state_dict ( self , state_dict ) : self . optimizer . load_state_dict ( state_dict ) # override learning rate, momentum, etc. with latest values for group in self . optimizer . param_groups : group . update ( self . params )