tf.keras.datasets.cifar10.load_data
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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 |
|
|
|
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 |
|---|
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 0-9)
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)
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.datasets.cifar10.load_data\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/datasets/cifar10.py#L13-L99) |\n\nLoads the CIFAR10 dataset. \n\n tf.keras.datasets.cifar10.load_data()\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|----------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|\n| - [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) | - [Convolutional Neural Network (CNN)](https://www.tensorflow.org/tutorials/images/cnn) |\n\nThis is a dataset of 50,000 32x32 color training images and 10,000 test\nimages, labeled over 10 categories. See more info at the\n[CIFAR homepage](https://www.cs.toronto.edu/%7Ekriz/cifar.html).\n\n#### The classes are:\n\n| Label | Description |\n|-------|-------------|\n| 0 | airplane |\n| 1 | automobile |\n| 2 | bird |\n| 3 | cat |\n| 4 | deer |\n| 5 | dog |\n| 6 | frog |\n| 7 | horse |\n| 8 | ship |\n| 9 | truck |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`. ||\n\n\u003cbr /\u003e\n\n**`x_train`** : `uint8` NumPy array of grayscale image data with shapes\n`(50000, 32, 32, 3)`, containing the training data. Pixel values range\nfrom 0 to 255.\n\n**`y_train`** : `uint8` NumPy array of labels (integers in range 0-9)\nwith shape `(50000, 1)` for the training data.\n\n**`x_test`** : `uint8` NumPy array of grayscale image data with shapes\n`(10000, 32, 32, 3)`, containing the test data. Pixel values range\nfrom 0 to 255.\n\n**`y_test`** : `uint8` NumPy array of labels (integers in range 0-9)\nwith shape `(10000, 1)` for the test data.\n\n#### Example:\n\n (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n assert x_train.shape == (50000, 32, 32, 3)\n assert x_test.shape == (10000, 32, 32, 3)\n assert y_train.shape == (50000, 1)\n assert y_test.shape == (10000, 1)"]]
View source on GitHub