2.5 KiB
mnists
downloads and prepares various mnist-compatible datasets.
files are downloaded to ~/.mnist
and checked for integrity by SHA-256 hashes.
dependencies
python 3.6 (or later), numpy.
install
pip install --upgrade --upgrade-strategy only-if-needed 'https://github.com/notwa/mnists/tarball/master#egg=mnists'
I've added --upgrade-strategy to the command-line
so you don't accidentally "upgrade" numpy to
a version not compiled specifically for your system.
This can happen when using e.g. Anaconda.
usage
import mnists
dataset = "emnist_balanced"
train_images, train_labels, test_images, test_labels = mnists.prepare(dataset)
the default images shape is (n, 1, 28, 28).
pass flatten=True to mnists.prepare to get (n, 784).
datasets
in alphabetical order:
emnist
-
emnist_balanced
train images shape: (112800, 1, 28, 28)
train labels shape: (112800, 47)
test images shape: (18800, 1, 28, 28)
test labels shape: (18800, 47) -
emnist_byclass
train images shape: (697932, 1, 28, 28)
train labels shape: (697932, 62)
test images shape: (116323, 1, 28, 28)
test labels shape: (116323, 62) -
emnist_bymerge
train images shape: (697932, 1, 28, 28)
train labels shape: (697932, 47)
test images shape: (116323, 1, 28, 28)
test labels shape: (116323, 47) -
emnist_digits
train images shape: (240000, 1, 28, 28)
train labels shape: (240000, 10)
test images shape: (40000, 1, 28, 28)
test labels shape: (40000, 10) -
emnist_letters
train images shape: (124800, 1, 28, 28)
train labels shape: (124800, 26)
test images shape: (20800, 1, 28, 28)
test labels shape: (20800, 26) -
emnist_mnist
train images shape: (60000, 1, 28, 28)
train labels shape: (60000, 10)
test images shape: (10000, 1, 28, 28)
test labels shape: (10000, 10)
fashion-mnist
fashion_mnist
train images shape: (60000, 1, 28, 28)
train labels shape: (60000, 10)
test images shape: (10000, 1, 28, 28)
test labels shape: (10000, 10)
mnist
mnist
train images shape: (60000, 1, 28, 28)
train labels shape: (60000, 10)
test images shape: (10000, 1, 28, 28)
test labels shape: (10000, 10)