MNIST

class olympus.datasets.mnist.MNIST(data_path, mini=False, train_size=None, valid_size=None, test_size=None, input_shape=None, target_shape=None, **kwargs)

Bases: olympus.datasets.dataset.AllDataset

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. More on wikipedia.

The full specification can be found at here. See also BalancedEMNIST and FashionMNIST

References

[1]	Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE, 86(11):2278-2324, November 1998.

Attributes:

classes: List[int]

Return the mapping between samples index and their class

input_shape: (28, 28)

Size of a sample returned after transformation

target_shape: (10,)

The classes are numbers from 0 to 9

train_size: 50000

Size of the train dataset

valid_size: 10000

Size of the validation dataset

test_size: 10000

Size of the test dataset

Methods

categories()	Dataset tags so we can filter what we want depending on the task
transforms()

register_datapipe_as_function
register_function

static categories()

Dataset tags so we can filter what we want depending on the task