/Users/csian/CP/data_set/MNIST

import os

import struct

import numpy as np

def load_mnist(path, kind='train'):

labels_path=os.path.join(path, '%s-labels-idx1-ubyte' %kind)

images_path=os.path.join(path, '%s-images-idx3-ubyte' %kind)

with open(labels_path, 'rb') as lbpath:

magic, n=struct.unpack('>II', lbpath.read(8))

labels=np.fromfile(lbpath, dtype=np.uint8)

with open(images_path, 'rb') as imgpath:

magic, num, rows, cols=struct.unpack(">IIII", imgpath.read(16))

images=np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)

images=((images/255.)-.5)*2

return images, labels

load data sets

X_train, y_train, X_test, y_test=[mnist[f] for f in mnist.files]

>>> X_train, y_train, X_test, y_test=[mnist[f] for f in mnist.files]

from sklearn fetch_openml get MNIST data set

from sklearn.datasets import fetch_openml

from sklearn.model_selection import train_test_split

X, y=fetch_openml('mnist_784', version=1, return_X_y=True)

y=y.astype(int)

X=((X/255.)-.0)*2

X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=10000, random_state=123, stratify=y)