chainer.datasets.get_fashion_mnist(withlabel=True, ndim=1, scale=1.0, dtype=None, label_dtype=<class 'numpy.int32'>, rgb_format=False)[source]

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 [0, scale].

This function returns the training set and the test set of the official Fashion-MNIST dataset. If withlabel is 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 as follows:

    • ndim == 1: the shape is (784,)

    • ndim == 2: the shape is (28, 28)

    • ndim == 3: the shape is (1, 28, 28)

  • scale (float) – Pixel value scale. If it is 1 (default), pixels are scaled to the interval [0, 1].

  • dtype – Data type of resulting image arrays. chainer.config.dtype is used by default (see Configuring Chainer).

  • label_dtype – Data type of the labels.

  • rgb_format (bool) – if ndim == 3 and rgb_format is True, the image will be converted to rgb format by duplicating the channels so the image shape is (3, 28, 28). Default is False.


A tuple of two datasets. If withlabel is True, both datasets are TupleDataset instances. Otherwise, both datasets are arrays of images.