IterableTorchMnistDataset#

class IterableTorchMnistDataset[source]#

Iterable MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.

Parameters

batch_size: int, default=64: Batch size to use
train: bool, default: true: If True, creates dataset from training.pt, otherwise from test.pt.
transform: t.Optional[t.Callable], default: None: A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop
n_samples: int, default: None: Number of samples to use. If None, use all samples.

__init__(batch_size: int = 64, train: bool = True, transform: Optional[Callable] = None, n_samples: Optional[int] = None) → None[source]#

__new__(*args, **kwargs)#

Attributes

`IterableTorchMnistDataset.functions`
`IterableTorchMnistDataset.mirrors`
`IterableTorchMnistDataset.raw_folder`	Return the path to the raw data folder.
`IterableTorchMnistDataset.reduce_ex_hook`
`IterableTorchMnistDataset.resources`
`IterableTorchMnistDataset.type`

Methods

`IterableTorchMnistDataset.download`()	Download the MNIST data if it doesn't exist already.
`IterableTorchMnistDataset.register_datapipe_as_function`(...)
`IterableTorchMnistDataset.register_function`(...)
`IterableTorchMnistDataset.reinforce_type`(...)	Reinforce the type for DataPipe instance.
`IterableTorchMnistDataset.set_reduce_ex_hook`(hook_fn)

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TorchMnistDataset.register_function

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IterableTorchMnistDataset.functions