使用邏輯回歸進行MNIST資料集分類

mnist資料集是機器學習領域中非常經典的乙個資料集，由60000個訓練樣本和10000個測試樣本組成，每個樣本都是一張28 * 28畫素的灰度手寫數字。

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     #從openml.org**匯入資料
from sklearn.utils import check_random_state
t0=time.time()
train_samples=5000
#載入資料集
x, y = fetch_openml('mnist_784', version=1, return_x_y=true)
print(x.shape,y.shape)
#視覺化樣本，圖形化顯示前6個資料
permutation = random_state.permutation(x.shape[0])    #隨機排序序列
x = x[permutation]
y = y[permutation]
x = x.reshape((x.shape[0], -1))
x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=train_samples,test_size=10000)
#資料進行標準化處理
scaler=standardscaler()
x_train=scaler.fit_transform(x_train)
x_test=scaler.transform(x_test)
#訓練模型
clf=logisticregression(c=50. / train_samples, penalty='l1', solver='saga', tol=0.1)
clf.fit(x_train,y_train)
score=clf.score(x_test,y_test)
sparsity = np.mean(clf.coef_ == 0) * 100      #稀疏性
print("sparsity with l1 penalty: %.2f%%" % sparsity)
print("test score with l1 penalty: %.4f" % score)
#畫圖,為什麼要給係數畫圖呢？
coef = clf.coef_.copy()  
print(coef.shape)
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()

問題：為什麼要給係數畫圖呢？

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