View source on GitHub
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Loads the CIFAR10 dataset.
tf.keras.datasets.cifar10.load_data()
Used in the notebooks
| Used in the guide | Used in the tutorials |
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This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 10 categories. See more info at the CIFAR homepage.
The classes are:
| Label | Description |
|---|---|
| 0 | airplane |
| 1 | automobile |
| 2 | bird |
| 3 | cat |
| 4 | deer |
| 5 | dog |
| 6 | frog |
| 7 | horse |
| 8 | ship |
| 9 | truck |
Returns | |
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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 0-9)
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 0-9)
with shape (10000, 1) for the test data.
Example:
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.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)