View source on GitHub
|
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 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).
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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)