mnist handwritten digits dataset import numpy as np from keras apiexport import kerasexport from keras utils fileutils import getfile kerasexportkeras datasets mnist loaddata def loaddatapathmnist npz originfolder https storage googleapis comtensorflowtfkerasdatasets path getfile fnamepath originoriginfolder mnist npz filehash noqa e501 731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1 with np loadpath allowpickletrue as f xtrain ytrain fxtrain fytrain xtest ytest fxtest fytest return xtrain ytrain xtest ytest 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 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 noqa e501
import numpy as np

from keras.api_export import keras_export
from keras.utils.file_utils import get_file


@keras_export("keras.datasets.mnist.load_data")
def load_data(path="mnist.npz"):
    
    origin_folder = (
        "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
    )
    path = get_file(
        fname=path,
        origin=origin_folder + "mnist.npz",
        file_hash=(  
            "731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1"
        ),
    )
    with np.load(path, allow_pickle=True) as f:
        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)