load_dataset#

load_dataset(train: bool = True, batch_size: Optional[int] = None, shuffle: bool = True, pin_memory: bool = True, object_type: Literal['VisionData', 'DataLoader'] = 'DataLoader') → Union[DataLoader, ClassificationData][source]#

Download MNIST dataset.

Parameters
trainbool, default True

Train or Test dataset

batch_size: int, optional

how many samples per batch to load

shufflebool, default True

to reshuffled data at every epoch or not

pin_memorybool, default True

If True, the data loader will copy Tensors into CUDA pinned memory before returning them.

object_typeLiteral[Dataset, DataLoader], default ‘DataLoader’

object type to return. if ‘VisionData’ then deepchecks.vision.VisionData will be returned, if ‘DataLoader’ then torch.utils.data.DataLoader

Returns
Union[deepchecks.vision.VisionData, torch.utils.data.DataLoader]

depending on the object_type parameter value, instance of deepchecks.vision.VisionData or torch.utils.data.DataLoader will be returned

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mnist

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load_model