dataeval.utils.dataset.datasets.MNIST
=====================================

.. py:class:: dataeval.utils.dataset.datasets.MNIST(root, train = True, download = False, size = -1, unit_interval = False, dtype = None, channels = None, flatten = False, normalize = None, corruption = None, classes = None, balance = True, randomize = True, slice_back = False, verbose = True)



   MNIST Dataset and Corruptions.

   :param root: str | ``pathlib.Path``
                Root directory of dataset where the ``mnist_c/`` folder exists.
   :param train: bool, default True
                 If True, creates dataset from ``train_images.npy`` and ``train_labels.npy``.
   :param download: bool, default False
                    If True, downloads the dataset from the internet and puts it in root
                    directory. If dataset is already downloaded, it is not downloaded again.
   :param size: int, default -1
                Limit the dataset size, must be a value greater than 0.
   :param unit_interval: bool, default False
                         Shift the data values to the unit interval [0-1].
   :param dtype: type | None, default None
                 Change the :term:`NumPy` dtype - data is loaded as np.uint8
   :param channels: Literal['channels_first' | 'channels_last'] | None, default None
                    Location of channel axis if desired, default has no channels (N, 28, 28)
   :param flatten: bool, default False
                   Flatten data into single dimension (N, 784) - cannot use both channels and flatten,
                   channels takes priority over flatten.
   :param normalize: tuple[mean, std] | None, default None
                     Normalize images acorrding to provided mean and standard deviation
   :param corruption: Literal['identity' | 'shot_noise' | 'impulse_noise' | 'glass_blur' |
                      'motion_blur' | 'shear' | 'scale' | 'rotate' | 'brightness' | 'translate' | 'stripe' |
                      'fog' | 'spatter' | 'dotted_line' | 'zigzag' | 'canny_edges'] | None, default None
                      The desired corruption style or None.
   :param classes: Literal["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
                   | int | list[int] | list[Literal["zero", "one", "two", "three", "four", "five", "six", "seven",
                   "eight", "nine"]] | None, default None
                   Option to select specific classes from dataset.
   :param balance: bool, default True
                   If True, returns equal number of samples for each class.
   :param randomize: bool, default True
                     If True, shuffles the data prior to selection - uses a set seed for reproducibility.
   :param slice_back: bool, default False
                      If True and size has a value greater than 0, then grabs selection starting at the last image.
   :param verbose: bool, default True
                   If True, outputs print statements.