# tf.keras.datasets.fashion_mnist.load_data

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/datasets/fashion_mnist.py#L28-L111) |

Loads the Fashion-MNIST dataset.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.datasets.fashion_mnist.load_data\`

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    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 |

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| ## 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
`(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,)

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| ## License ||
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| 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). ||

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