- chainer.datasets.get_mnist(withlabel=True, ndim=1, scale=1.0, dtype=None, label_dtype=<class 'numpy.int32'>, rgb_format=False)¶
Gets the MNIST dataset.
MNIST is a set of hand-written digits represented by grey-scale 28x28 images. In the original images, each pixel is represented by one-byte unsigned integer. This function scales the pixels to floating point values in the interval
This function returns the training set and the test set of the official MNIST dataset. If
True, each dataset consists of tuples of images and labels, otherwise it only consists of images.
withlabel (bool) – If
True, it returns datasets with labels. In this case, each example is a tuple of an image and a label. Otherwise, the datasets only contain images.
ndim (int) –
Number of dimensions of each image. The shape of each image is determined depending on
ndim == 1: the shape is
ndim == 2: the shape is
ndim == 3: the shape is
(1, 28, 28)
scale (float) – Pixel value scale. If it is 1 (default), pixels are scaled to the interval
dtype – Data type of resulting image arrays.
chainer.config.dtypeis used by default (see Configuring Chainer).
label_dtype – Data type of the labels.
rgb_format (bool) – if
ndim == 3and
True, the image will be converted to rgb format by duplicating the channels so the image shape is (3, 28, 28). Default is
A tuple of two datasets. If
True, both datasets are
TupleDatasetinstances. Otherwise, both datasets are arrays of images.