tf.keras.datasets.fashion_mnist.load_data
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Loads the Fashion-MNIST dataset.
tf.keras.datasets.fashion_mnist.load_data()
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
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 |
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 2024-06-07 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 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/v3.3.3/keras/src/datasets/fashion_mnist.py#L12-L96) |\n\nLoads the Fashion-MNIST dataset. \n\n tf.keras.datasets.fashion_mnist.load_data()\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [tf.data: Build TensorFlow input pipelines](https://www.tensorflow.org/guide/data) | - [Intro to Autoencoders](https://www.tensorflow.org/tutorials/generative/autoencoder) - [Introduction to the Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner) - [Quantum data](https://www.tensorflow.org/quantum/tutorials/quantum_data) - [Examining the TensorFlow Graph](https://www.tensorflow.org/tensorboard/graphs) |\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\n\u003cbr /\u003e\n\nTuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.\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#### License:\n\nThe copyright for Fashion-MNIST is held by Zalando SE.\nFashion-MNIST is licensed under the [MIT license](https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE)."]]
View source on GitHub