Datasets:
fashion_mnist

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""FashionMNIST Data Set"""

	from __future__ import absolute_import, division, print_function

	import struct

	import numpy as np

	import datasets


	_CITATION = """\
	@article{DBLP:journals/corr/abs-1708-07747,
	author = {Han Xiao and
	Kashif Rasul and
	Roland Vollgraf},
	title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
	Algorithms},
	journal = {CoRR},
	volume = {abs/1708.07747},
	year = {2017},
	url = {http://arxiv.org/abs/1708.07747},
	archivePrefix = {arXiv},
	eprint = {1708.07747},
	timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
	60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
	associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
	replacement for the original MNIST dataset for benchmarking machine learning algorithms.
	It shares the same image size and structure of training and testing splits.
	"""

	_HOMEPAGE = "https://github.com/zalandoresearch/fashion-mnist"
	_LICENSE = "https://raw.githubusercontent.com/zalandoresearch/fashion-mnist/master/LICENSE"

	_URL = "https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/"
	_URLS = {
	"train_images": "train-images-idx3-ubyte.gz",
	"train_labels": "train-labels-idx1-ubyte.gz",
	"test_images": "t10k-images-idx3-ubyte.gz",
	"test_labels": "t10k-labels-idx1-ubyte.gz",
	}


	class FashionMnist(datasets.GeneratorBasedBuilder):
	"""FashionMNIST Data Set"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="fashion_mnist",
	version=datasets.Version("1.0.0"),
	description=_DESCRIPTION,
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Array2D(shape=(28, 28), dtype="uint8"),
	"label": datasets.features.ClassLabel(
	names=[
	"T - shirt / top",
	"Trouser",
	"Pullover",
	"Dress",
	"Coat",
	"Sandal",
	"Shirt",
	"Sneaker",
	"Bag",
	"Ankle boot",
	]
	),
	}
	),
	supervised_keys=("image", "label"),
	homepage="https://github.com/zalandoresearch/fashion-mnist",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": [downloaded_files["train_images"], downloaded_files["train_labels"]],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": [downloaded_files["test_images"], downloaded_files["test_labels"]],
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""This function returns the examples in the raw form."""
	# Images
	with open(filepath[0], "rb") as f:
	# First 16 bytes contain some metadata
	_ = f.read(4)
	size = struct.unpack(">I", f.read(4))[0]
	_ = f.read(8)
	images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)

	# Labels
	with open(filepath[1], "rb") as f:
	# First 8 bytes contain some metadata
	_ = f.read(8)
	labels = np.frombuffer(f.read(), dtype=np.uint8)

	for idx in range(size):
	yield idx, {"image": images[idx], "label": int(labels[idx])}