TensorFlow 1 version | View source on GitHub |
Loads CIFAR100 dataset.
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 the y_train, y_test: uint8 arrays of category labels with shape (num_samples, 1). |
Raises | |
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ValueError
|
in case of invalid label_mode .
|