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, 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)