meta_model.py

@TrainerBase.register('metatrainer')
class MetaTrainer(Trainer):
    def __init__(self,
                 model: Model,
                 meta_model: MetaModel,
                 optimizer: torch.optim.Optimizer,
                 iterator: DataIterator,
                 train_datasets: List[Iterable[Instance]],
                 validation_datasets: Optional[Iterable[Instance]] = None,
                 # meta learner parameters
                 meta_batches: int = 200,
                 inner_steps: int = 3,
                 tasks_per_batch: int = 2,
                 batch_norm = True,
                 **kwargs) -> None:
                
        """
        A metatrainer for doing meta-learning. It just takes a list of labeled datasets
        and a ``DataIterator``, and uses the supplied meta-learner to learn the weights
        for your model over some fixed number of epochs. You can also pass in a validation
        datasets and enable early stopping. There are many other bells and whistles as well.

        Parameters
        ----------
        model : ``Model``, required.
          
        """

        # I am not calling move_to_gpu here, because if the model is
        # not already on the GPU then the optimizer is going to be wrong.
        super().__init__(model, optimizer, iterator, train_datasets, **kwargs)
        self.train_data = train_datasets
        self._validation_data = validation_datasets
        # Meta Trainer specific params
        self.meta_batches = meta_batches
        self.tasks_per_batch = tasks_per_batch
        self.inner_steps = inner_steps
        self.step_size = .01
        self.batch_norm = batch_norm