get_fashion_mnist(withlabel=True, ndim=1, scale=1.0, dtype=None, label_dtype=<class 'numpy.int32'>, rgb_format=False)¶
Gets the Fashion-MNIST dataset.
Fashion-MNIST is a set of fashion articles 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 Fashion-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.