trw.datasets.mnist

Module Contents

Functions

identity(batch)

create_mnist_dataset(batch_size: int = 1000, root: str = None, transforms: List[trw.transforms.Transform] = None, nb_workers: int = 5, data_processing_batch_size: int = 200, normalize_0_1: bool = False, select_classes_train: Optional[Sequence[int]] = None, select_classes_test: Optional[Sequence[int]] = None) → Tuple[trw.basic_typing.Datasets, trw.basic_typing.DatasetsInfo]

param batch_size
trw.datasets.mnist.identity(batch)
trw.datasets.mnist.create_mnist_dataset(batch_size: int = 1000, root: str = None, transforms: List[trw.transforms.Transform] = None, nb_workers: int = 5, data_processing_batch_size: int = 200, normalize_0_1: bool = False, select_classes_train: Optional[Sequence[int]] = None, select_classes_test: Optional[Sequence[int]] = None) → Tuple[trw.basic_typing.Datasets, trw.basic_typing.DatasetsInfo]
Parameters

  • batch_size –

  • root –

  • transforms –

  • nb_workers –

  • data_processing_batch_size –

  • normalize_0_1 –

  • select_classes_train – a subset of classes to be selected for the training split

  • select_classes_test – a subset of classes to be selected for the test split

Returns: