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
ofNumPy 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)