tf.keras.datasets.cifar100.load_data

Loads the CIFAR100 dataset.

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tf.compat.v1.keras.datasets.cifar100.load_data

tf.keras.datasets.cifar100.load_data(
    label_mode='fine'
)

This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes. See more info at the CIFAR homepage.

Args
label_mode	one of "fine", "coarse". If it is "fine" the category labels are the fine-grained labels, if it is "coarse" the output labels are the coarse-grained superclasses.

Returns
Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test).

x_train: uint8 NumPy array of grayscale image data with shapes (50000, 32, 32, 3), containing the training data. Pixel values range from 0 to 255.

y_train: uint8 NumPy array of labels (integers in range 0-99) with shape (50000, 1) for the training data.

x_test: uint8 NumPy array of grayscale image data with shapes (10000, 32, 32, 3), containing the test data. Pixel values range from 0 to 255.

y_test: uint8 NumPy array of labels (integers in range 0-99) with shape (10000, 1) for the test data.

Example:

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
assert x_train.shape == (50000, 32, 32, 3)
assert x_test.shape == (10000, 32, 32, 3)
assert y_train.shape == (50000, 1)
assert y_test.shape == (10000, 1)