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