tf.keras.datasets.cifar10.load

TensorFlow 1 version

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Loads 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.

Arguments
`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, x_test: uint8 arrays of RGB image data with shape `(num_samples, 3, 32, 32)` if `tf.keras.backend.image_data_format()` is `'channels_first'`, or `(num_samples, 32, 32, 3)` if the data format is `'channels_last'`. y_train, y_test: uint8 arrays of category labels with shape (num_samples, 1).

Returns

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

x_train, x_test: uint8 arrays of RGB image data with shape (num_samples, 3, 32, 32) if tf.keras.backend.image_data_format() is 'channels_first', or (num_samples, 32, 32, 3) if the data format is 'channels_last'.

y_train, y_test: uint8 arrays of category labels with shape (num_samples, 1).

Raises
`ValueError`	in case of invalid `label_mode`.

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tf.keras / datasets / datasets.cifar10.load_data

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