使用sklearn進行mnist資料集分類

深度之眼-西瓜書課後**

import time

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.linear_model import logisticregression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import  standardscaler
from sklearn.utils import check_random_state
print
(__doc__)
t0 = time.time(
)train_sample =
5000
x,y = fetch_openml(
'mnist_784'
,version=
1,return_x_y=
true
)random_state = check_random_state(0)
#設定隨機種子
permutation = random_state.permutation(x.shape[0]
)x = x[permutation]
y = y[permutation]
#兩個進行順序打亂
# x = x.reshape((x.shape[0],-1))
x_train,x_test,y_train,y_test = train_test_split(x, y
, train_size=train_sample
, test_size=
10000
)scaler = standardscaler(
)#對資料進行歸一化，即對資料求方差與均值
x_train = scaler.fit_transform(x_train)
#先進行擬合，再進行歸一化變換
x_test = scaler.transform(x_test)
#只是進行歸一化變換
clf = logisticregression(c=50.
/train_sample
, multi_class=
'multinomial'
,penalty=
'l1'
,solver=
'saga'
,tol=
0.1)
clf.fit(x_train,y_train)
sparsity = np.mean(clf.coef_ ==0)
*100
score = clf.score(x_test,y_test)
print
('sparsity with l1 penalty: %.2f%%'
% sparsity)
#76.82%
print
('sparsity with l1 penalty %'
.format
(sparsity)
)#76.82%
print
('test score with l1 penalty: %.4f'
% score)
# 0.8287
print
('test score with l1 penalty '
.format
(score)
)# 0.8287
coef = clf.coef_.copy(
)plt.figure(figsize=(10
,5))
scale = np.
abs(coef)
.max()
for i in
range(10
):l1_plot = plt.subplot(2,
5, i +1)
l1_plot.imshow(coef[i]
.reshape(28,
28),interpolation=
'nearest'
,cmap=plt.cm.rdbu
,vmin=
-scale
,vmax=scale)
l1_plot.set_xticks(()
)    l1_plot.set_yticks(()
)    l1_plot.set_xlabel(
'class %i'
% i)
plt.suptitle(
'classification vector for .....'
)run_time = time.time(
)-t0
print
('example run in %.3f s'
% run_time)
plt.show(
)

如果要使用fetch_openml，建議進行一下科學上網

裡面同時對%和format兩者輸出形式進行了對比

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