1.普通SVM分類MNIST資料集

 1 #匯入必備的包
 2 import numpy as np
 3 import struct
 4 import matplotlib.pyplot as plt
 5 import os
 6 ##載入svm模型
 7 from sklearn import svm
 8 ###用於做資料預處理
 9 from sklearn import preprocessing
10 import time
11 
12 #載入資料的路徑
13 path='./dataset/mnist/raw'
14 def load_mnist_train(path, kind='
 
train'):    
15     labels_path = os.path.join(path,'%s-labels-idx1-ubyte'% kind)
16     images_path = os.path.join(path,'%s-images-idx3-ubyte'% kind)
17     with open(labels_path, 'rb') as lbpath:
18         magic, n = struct.unpack('>II',lbpath.read(8))
19         labels = np.fromfile(lbpath,dtype=np.uint8)
 
20     with open(images_path, 'rb') as imgpath:
21         magic, num, rows, cols = struct.unpack('>IIII',imgpath.read(16))
22         images = np.fromfile(imgpath,dtype=np.uint8).reshape(len(labels), 784)
23     return images, labels
24 def load_mnist_test(path, kind='t10k'):
25     labels_path = os.path.join(path,'
 
%s-labels-idx1-ubyte'% kind)
26     images_path = os.path.join(path,'%s-images-idx3-ubyte'% kind)
27     with open(labels_path, 'rb') as lbpath:
28         magic, n = struct.unpack('>II',lbpath.read(8))
29         labels = np.fromfile(lbpath,dtype=np.uint8)
30     with open(images_path, 'rb') as imgpath:
31         magic, num, rows, cols = struct.unpack('>IIII',imgpath.read(16))
32         images = np.fromfile(imgpath,dtype=np.uint8).reshape(len(labels), 784)
33     return images, labels   
34 train_images,train_labels=load_mnist_train(path)
35 test_images,test_labels=load_mnist_test(path)
36 
37 X=preprocessing.StandardScaler().fit_transform(train_images)  
38 X_train=X[0:60000]
39 y_train=train_labels[0:60000]
40 
41 print(time.strftime('%Y-%m-%d %H:%M:%S'))
42 model_svc = svm.LinearSVC()
43 #model_svc = svm.SVC()
44 model_svc.fit(X_train,y_train)
45 print(time.strftime('%Y-%m-%d %H:%M:%S'))
46 
47 ##顯示前30個樣本的真實標籤和預測值，用圖顯示
48 x=preprocessing.StandardScaler().fit_transform(test_images)
49 x_test=x[0:10000]
50 y_pred=test_labels[0:10000]
51 print(model_svc.score(x_test,y_pred))
52 y=model_svc.predict(x)
53 
54 fig1=plt.figure(figsize=(8,8))
55 fig1.subplots_adjust(left=0,right=1,bottom=0,top=1,hspace=0.05,wspace=0.05)
56 for i in range(100):
57     ax=fig1.add_subplot(10,10,i+1,xticks=[],yticks=[])
58     ax.imshow(np.reshape(test_images[i], [28,28]),cmap=plt.cm.binary,interpolation='nearest')
59     ax.text(0,2,"pred:"+str(y[i]),color='red')
60     #ax.text(0,32,"real:"+str(test_labels[i]),color='blue')
61 plt.show()

2.執行結果：

開始時間：2018-11-17 08:31:09

結束時間：2018-11-17 08:53:04

用時：21分55秒

精度：0.9122

預測圖片：