tf.keras.datasets.fashion_mnist.load_data
Stay organized with collections
Save and categorize content based on your preferences.
Loads the Fashion-MNIST dataset.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.datasets.fashion_mnist.load_data`
tf.keras.datasets.fashion_mnist.load_data()
This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories,
along with a test set of 10,000 images. This dataset can be used as
a drop-in replacement for MNIST.
The classes are:
Label |
Description |
0 |
T-shirt/top |
1 |
Trouser |
2 |
Pullover |
3 |
Dress |
4 |
Coat |
5 |
Sandal |
6 |
Shirt |
7 |
Sneaker |
8 |
Bag |
9 |
Ankle boot |
Returns |
Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test) .
|
x_train: uint8 NumPy array of grayscale image data with shapes
(60000, 28, 28)
, containing the training data.
y_train: uint8 NumPy array of labels (integers in range 0-9)
with shape (60000,)
for the training data.
x_test: uint8 NumPy array of grayscale image data with shapes
(10000, 28, 28), containing the test data.
y_test: uint8 NumPy array of labels (integers in range 0-9)
with shape (10000,)
for the test data.
Example:
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
assert x_train.shape == (60000, 28, 28)
assert x_test.shape == (10000, 28, 28)
assert y_train.shape == (60000,)
assert y_test.shape == (10000,)
License |
The copyright for Fashion-MNIST is held by Zalando SE.
Fashion-MNIST is licensed under the MIT license.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06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.fashion_mnist.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/fashion_mnist.py#L28-L111) |\n\nLoads the Fashion-MNIST 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.fashion_mnist.load_data\\`\n\n\u003cbr /\u003e\n\n tf.keras.datasets.fashion_mnist.load_data()\n\nThis is a dataset of 60,000 28x28 grayscale images of 10 fashion categories,\nalong with a test set of 10,000 images. This dataset can be used as\na drop-in replacement for MNIST.\n\n#### The classes are:\n\n| Label | Description |\n|-------|-------------|\n| 0 | T-shirt/top |\n| 1 | Trouser |\n| 2 | Pullover |\n| 3 | Dress |\n| 4 | Coat |\n| 5 | Sandal |\n| 6 | Shirt |\n| 7 | Sneaker |\n| 8 | Bag |\n| 9 | Ankle boot |\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`(60000, 28, 28)`, containing the training data.\n\n**y_train** : uint8 NumPy array of labels (integers in range 0-9)\nwith shape `(60000,)` for the training data.\n\n**x_test**: uint8 NumPy array of grayscale image data with shapes\n(10000, 28, 28), containing the test data.\n\n**y_test** : uint8 NumPy array of labels (integers in range 0-9)\nwith shape `(10000,)` for the test data.\n\n#### Example:\n\n (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n assert x_train.shape == (60000, 28, 28)\n assert x_test.shape == (10000, 28, 28)\n assert y_train.shape == (60000,)\n assert y_test.shape == (10000,)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| License ------- ||\n|---|---|\n| The copyright for Fashion-MNIST is held by Zalando SE. Fashion-MNIST is licensed under the [MIT license](https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE). ||\n\n\u003cbr /\u003e"]]