tf.keras.datasets.cifar100.load_data | TensorFlow v2.16.1 (original) (raw)
tf.keras.datasets.cifar100.load_data
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Loads the 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 theCIFAR homepage.
| Args | |
|---|---|
| label_mode | one of "fine", "coarse". If it is "fine", the category labels are the fine-grained labels, and 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)