f7fd8a8395cbb90973dbd3ab7b55cda80865b656,tensorflow_datasets/image/cifar.py,Cifar100,_info,#Cifar100#,173

Before Change

    )

  def _info(self):
    cifar_shape = (_CIFAR_IMAGE_SIZE, _CIFAR_IMAGE_SIZE, 3)
    label_to_use = "coarse_labels" if self._use_coarse_labels else "fine_labels"
    return tfds.core.DatasetInfo(
        features=tfds.features.FeaturesDict({
            "image": tfds.features.Image(shape=cifar_shape),
            "label": tfds.features.OneOf(choice=label_to_use, feature_dict={
                "coarse_labels": tfds.features.ClassLabel(num_classes=20),
                "fine_labels": tfds.features.ClassLabel(num_classes=100),
            }),
        }),
        supervised_keys=("image", "label"),
    )


class CifarInfo(collections.namedtuple("_CifarInfo", [
    "name",

After Change

  def _info(self):
    label_to_use = "coarse_labels" if self._use_coarse_labels else "fine_labels"
    return tfds.core.DatasetInfo(
        name=self.name,
        description=("This dataset is just like the CIFAR-10, except it has "
                     "100 classes containing 600 images each. There are 500 "
                     "training images and 100 testing images per class. The "
                     "100 classes in the CIFAR-100 are grouped into 20 "
                     "superclasses. Each image comes with a \"fine\" label "
                     "(the class to which it belongs) and a \"coarse\" label "
                     "(the superclass to which it belongs)."),
        features=tfds.features.FeaturesDict({
            "image": tfds.features.Image(shape=_CIFAR_IMAGE_SHAPE),
            "label": tfds.features.OneOf(choice=label_to_use, feature_dict={
                "coarse_labels": tfds.features.ClassLabel(num_classes=20),
                "fine_labels": tfds.features.ClassLabel(num_classes=100),
            }),
        }),
        supervised_keys=("image", "label"),
        urls=["https://www.cs.toronto.edu/~kriz/cifar.html"],
        size_in_bytes=161.2 * tfds.core.units.MiB,
        citation=("Learning Multiple Layers of Features from Tiny Images, "
                  "Alex Krizhevsky, 2009. "
                  "https://www.cs.toronto.edu/~kriz/"
                  "learning-features-2009-TR.pdf")
    )


class CifarInfo(collections.namedtuple("_CifarInfo", [
    "name",

Instances