错误率：
载入训练数据... 载入完成，耗时3.197000秒 图片个数：42000，图片像素数目：784 载入测试数据... 载入完成，耗时2.214000秒 (33600L, 784L) (8400L, 784L) SVM开始训练... SVM训练结束，耗时2分钟21.261秒 SVM训练集准确率：94.676%，耗时3分钟55.097秒 SVM测试集准确率：93.810%，耗时0分钟57.619秒 # -*- coding:utf-8 -*- import numpy as np from sklearn import svm import matplotlib.colors import matplotlib.pyplot as plt from PIL import Image from sklearn.metrics import accuracy_score import pandas as pd import os import csv from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.ensemble import RandomForestClassifier from time import time from pprint import pprint def save_image(im,i): im=255-im a=im.astype(np.uint8) output_path='.\\HandWritten' if not os.path.exists(output_path): os.mkdir(output_path) Image.fromarray(a).save(output_path+('\\%d.png')%i) def save_result(model): data_test_hat=model.predict(data_test) with open('Prediction.csv','wb') as f: writer=csv.writer(f) writer.writerow(['ImageId','Label']) for i,d in enumerate(data_test_hat): writer.writerow([i,d]) if __name__=='__main__': classifier_type='SVM' print '载入训练数据...' t=time() data=pd.read_csv('.\\16.MNIST.train.csv',header=0,dtype=np.int) print '载入完成,耗时%f秒'%(time()-t) y=data['label'].values x=data.values[:,1:] print '图片个数:%d,图片像素数目:%d' % x.shape images=x.reshape(-1,28,28) y=y.ravel() print '载入测试数据...' t=time() data_test=pd.read_csv('.\\16.MNIST.test.csv',header=0,dtype=np.int) data_test=data_test.values images_test_result=data_test.reshape(-1,28,28) print '载入完成,耗时%f秒'%(time()-t) np.random.seed(0) #分隔训练集合测试集 x,x_test,y,y_test=train_test_split(x,y,train_size=0.8,random_state=1) images=x.reshape(-1,28,28) images_test=x_test.reshape(-1,28,28) print 'x.shape:',x.shape print 'x_test.shape:',x_test.shape matplotlib.rcParams['font.sans-serif'] = [u'SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False plt.figure(figsize=(15,9),facecolor='w') for index,image in enumerate(images[:16]): plt.subplot(4,8,index+1) plt.imshow(image,cmap=plt.cm.gray_r, interpolation='nearest') plt.title(u'训练图片：%i'%y[index]) for index,image in enumerate(images_test_result[:16]): plt.subplot(4,8,index+17) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') save_image(image.copy(),index) plt.title(u'测试图片') plt.tight_layout() plt.show() #SVM if classifier_type=='SVM': model=svm.SVC(C=1000,kernel='rbf', gamma=1e-10) print 'SVM开始训练...' t=time() model.fit(x,y) t=time()-t print 'SVM训练结束,耗时%d分钟%.3f秒'%(int(t/60),t-60*int(t/60)) # print '最优分类器：', model.best_estimator_ # print '最优参数：\t', model.best_params_ # print 'model.cv_results_ =' # pprint(model.cv_results_) t=time() y_hat=model.predict(x) t=time()-t print 'SVM训练集准确率:#.3f%%,耗时%d分钟%.3f秒'%(accuracy_score(y,y_hat)*100,int(t/60),t-60*int(t/60)) t=time() y_test_hat=model.predict(x_test) t=time()-t print 'SVM测试集准确率:%.3f%%,耗时%d分钟%.3f秒'%(accuracy_score(y_test,y_test_hat)*100,int(t/60),t-60*int(t/60)) save_result(model) elif classifier_type=='RF': rfc=RandomForestClassifier(100,criterion='gini',min_samples_split=2,min_impurity_split=1e-10,bootstrap=True,oob_score=True) print '随机森林开始训练...' t=time() rfc.fit(x,y) t=time()-t print '随机森林训练结束,耗时%d分钟%.3f秒'%(int(t/60),t-60*int(t/60)) print 'OOB准确率:%.3f%%'%(rfc.oob_score_*100) t=time() y_hat=rfc.predict(x) t=time()-t print '随机森林训练集准确率:%.3f%%,预测耗时:%d秒'%(accuracy_score(y,y_hat)*100,t) t=time() y_test_hat=rfc.predict(x_test) t=time()-t print '随机森林测试集准确率：%.3f%%,预测耗时:%d秒'%(accuracy_score(y_test,y_test_hat)*100,t) save_result(rfc) err = (y_test != y_test_hat) err_images = images_test[err] err_y_hat = y_test_hat[err] err_y = y_test[err] print err_y_hat print err_y plt.figure(figsize=(10, 8), facecolor='w') for index, image in enumerate(err_images): if index >= 12: break plt.subplot(3, 4, index + 1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title(u'错分为：%i，真实值：%i' % (err_y_hat[index], err_y[index])) plt.suptitle(u'数字图片手写体识别：分类器%s' % classifier_type, fontsize=18) plt.tight_layout(rect=(0, 0, 1, 0.95)) plt.show()
