使用逻辑回归进行MNIST数据集分类

MNIST数据集是机器学习领域中非常经典的一个数据集，由60000个训练样本和10000个测试样本组成，每个样本都是一张28 * 28像素的灰度手写数字图片。

import time

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn import datasets

from sklearn.preprocessing import StandardScaler

from sklearn.datasets import fetch_openml #从openml.org网站导入数据

from sklearn.utils import check_random_state

t0=time.time()

train_samples=5000

#载入数据集

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

print(X.shape,y.shape)

#可视化样本，图形化显示前6个数据

fig,ax=plt.subplots(nrows=2,ncols=3,sharex='all',sharey='all')

ax = ax.flatten()

for i in range(6):

img=X[i].reshape(28,28)

ax[i].matshow(img)

plt.show()