tf.keras.datasets.cifar100.load_data
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Loads the CIFAR100 dataset.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.datasets.cifar100.load_data`
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).
|
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)
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Last updated 2023-10-06 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.datasets.cifar100.load_data\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/datasets/cifar100.py#L29-L100) |\n\nLoads the CIFAR100 dataset.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n\\`tf.compat.v1.keras.datasets.cifar100.load_data\\`\n\n\u003cbr /\u003e\n\n tf.keras.datasets.cifar100.load_data(\n label_mode='fine'\n )\n\nThis is a dataset of 50,000 32x32 color training images and\n10,000 test images, labeled over 100 fine-grained classes that are\ngrouped into 20 coarse-grained classes. See more info at the\n[CIFAR homepage](https://www.cs.toronto.edu/%7Ekriz/cifar.html).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n\n\u003cbr /\u003e\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 0-99)\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 0-99)\nwith shape `(10000, 1)` for the test data.\n\n#### Example:\n\n (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.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