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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/src/datasets/mnist.py#L25-L86) |

Loads the MNIST dataset.

```python
tf.keras.datasets.mnist.load_data(
    path='mnist.npz'
)
```

This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
along with a test set of 10,000 images.
More info can be found at the
[MNIST homepage](http://yann.lecun.com/exdb/mnist/).

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| ## Args ||
|---|---|
| `path` | path where to cache the dataset locally (relative to `~/.keras/datasets`). |

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| ## Returns ||
|---|---|
| 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. Pixel values range
from 0 to 255.

**y_train** : uint8 NumPy array of digit 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. Pixel values range
from 0 to 255.

**y_test** : uint8 NumPy array of digit labels (integers in range 0-9)
with shape `(10000,)` for the test data.

#### Example:

```python
(x_train, y_train), (x_test, y_test) = keras.datasets.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 ||
|---|---|
| Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the [Creative Commons Attribution-Share Alike 3.0 license.](https://creativecommons.org/licenses/by-sa/3.0/) ||

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