Module keras.api.keras.datasets.mnist

Public API for tf.keras.datasets.mnist namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.datasets.mnist namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.datasets.mnist import load_data

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras.datasets.mnist", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def load_data(path='mnist.npz')

Loads the MNIST dataset.

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.

Args

path
path where to cache the dataset locally (relative to ~/.keras/datasets).

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. 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:

(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,)

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.

Expand source code
@keras_export('keras.datasets.mnist.load_data')
def load_data(path='mnist.npz'):
  """Loads the MNIST dataset.

  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/).

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

  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. 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,)
  ```

  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/)
  """
  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,
      origin=origin_folder + 'mnist.npz',
      file_hash=
      '731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1')
  with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg
    x_train, y_train = f['x_train'], f['y_train']
    x_test, y_test = f['x_test'], f['y_test']

    return (x_train, y_train), (x_test, y_test)