每一行代表一个手写字体图像，最大值为16，大小64，然后最后一列为该图片的标签值。

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 os from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from time import time def show_acc(a, b, tip): acc = a.ravel() == b.ravel() print('%s acc :%.2f%%' % (tip, 100*np.mean(acc))) def save_image(image, i): # 由于optdigits数据集的像素最大是16，所以这里对其reshape image *= 16.9 #　图像取反为了好观察 image = 255 - image # 转化为图像的uint8格式 a = image.astype(np.uint8) output_path = './/handwriting' if not os.path.exists(output_path): os.mkdir(output_path) Image.fromarray(a).save(output_path + ('//%d.jpg' % i)) if __name__ == '__main__': # 开始加载训练数据集 data = np.loadtxt('optdigits.tra', dtype=np.float, delimiter=',') # 最后一列得到的是该手写字体图片的label x, y = np.split(data, (-1,), axis=1) # 64x64大小 images = x.reshape(-1, 8, 8) y = y.ravel().astype(np.int) # 加载测试数据集 data_test = np.loadtxt('optdigits.tes', dtype=np.float, delimiter=',') x_test, y_test = np.split(data_test, (-1,), axis=1) images_test = x_test.reshape(-1, 8, 8) y_test = y_test.ravel().astype(np.int) plt.figure(figsize=(15, 15), 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('trian image:%i' %y[index]) for index, image in enumerate(images_test[:16]): plt.subplot(4, 8, index+17) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') save_image(image.copy(), index) plt.title('test image:%i' %y[index]) plt.tight_layout(1.5) plt.show() params = {'C':np.logspace(0, 3, 7), 'gamma':np.logspace(-5, 0, 11)} model = svm.SVC(C=10, kernel='rbf', gamma=0.001) print('==============start training=================') start = time() model.fit(x, y) end = time() train_time = end - start print('train time:%dseconds' % train_time) y_hat = model.predict(x) show_acc(y, y_hat, 'trian data') y_hat_test = model.predict(x_test) print('y_hat:\n', y_hat) print('y_test:\n', y_test) show_acc(y_test, y_hat_test, 'valiation data') # 测试集里面错分的数据 # 测试集里面和预测值不同的图像 err_images = images_test[y_test != y_hat_test] # 预测里面和测试不同的预测值 err_y_hat = y_hat_test[y_test != y_hat_test] # 测试里面和预测不同的测试值 err_y = y_test[y_test != y_hat_test] print('err_y_hat:\n', err_y_hat) print('err_y:\n', err_y) plt.figure(figsize=(15, 15), facecolor='w') for index, image in enumerate(err_images): if index >= 30: break plt.subplot(5, 6, index+1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('error:%i, the real:%i' % (err_y_hat[index], err_y[index])) plt.tight_layout(4) plt.show()

接着我们更换训练方法，修改程序：

# model = svm.SVC(C=10, kernel='rbf', gamma=0.001) model = GridSearchCV(svm.SVC(kernel='rbf'), param_grid=params, cv=3)

训练时间要长很多，但准确率并没有提升。。。。

接着我们使用经典的MNIST数据集来做实验：

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 import warnings def show_acc(a, b, tip): acc = a.ravel() == b.ravel() print('%s acc :%.2f%%' % (tip, 100*np.mean(acc))) def save_image(image, i): #　图像取反为了好观察 image = 255 - image # 转化为图像的uint8格式 a = image.astype(np.uint8) output_path = './/handwriting' if not os.path.exists(output_path): os.mkdir(output_path) Image.fromarray(a).save(output_path + ('//%d.jpg' % i)) def save_model(model): data_test_hat = model.predict(data_test) with open('Prediction.csv', 'wt') 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__': warnings.filterwarnings('ignore') classifier_type = 'RF' print('loading train data......') start = time() data = pd.read_csv('MNIST.train.csv', header=0, dtype=np.int) print('loading finishing......') # 读取标签值 y = data['label'].values x = data.values[:, 1:] print('the images numbers:%d, the pixs of images:%d' % (x.shape)) # reshape成28x28的格式，还原成原始的图像格式 images = x.reshape(-1, 28, 28) y = y.ravel() print(images) print(y) print('loading test data......') start = time() data_test = pd.read_csv('MNIST.test.csv', header=0, dtype=np.int) data_test = data_test.values images_test_result = data_test.reshape(-1, 28, 28) print('data-test:\t', data_test) print('images-test-result:\t', images_test_result) print('loading finishing......') np.random.seed(0) 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:\t', x.shape) print('x-test-shape:\t', x_test.shape) # 显示我们使用的部分训练数据和测试数据 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('train data:%d' % (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('test data') plt.tight_layout() plt.show() if classifier_type == 'SVM': model = svm.SVC(C=3000, kernel='rbf', gamma=1e-10) print('让我们荡起小浆，开始训练吧.............') t_start = time() model.fit(x, y) t_end = time() print('train time:%.3f' % (t_end - t_start)) print('小船到岸，清下水......') # print('最优分类器:', model.best_estimator_) # print('最优参数:\t', model.best_params_) # print('model.cv_results_ = \n', model.cv_results_) t = time() y_hat = model.predict(x) t = time() - t print('SVM训练集准确率:%.3f%%, 耗时:%.3f' %(accuracy_score(y, y_hat), t)) t = time() y_hat_test = model.predict(x_test) t = time() - t print('SVM测试集准确率:%.3f%%, 耗时:%.3f' %(accuracy_score(y_test, y_hat_test), t)) save_model(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('让我们再次荡起小浆，开始训练吧.............') t = time() rfc.fit(x, y) print('train time:%.3f' % (time() - t)) print('OOB准确率:%.3f%%' %(rfc.oob_score_*100)) print('小船到岸，清下水......') t = time() y_hat = rfc.predict(x) t = time() - t print('SVM训练集准确率:%.3f%%, 耗时:%.3f' %(accuracy_score(y, y_hat), t)) t = time() y_hat_test = rfc.predict(x_test) t = time() - t print('SVM测试集准确率:%.3f%%, 耗时:%.3f' %(accuracy_score(y_test, y_hat_test), t)) save_model(rfc) err = (y_test != y_hat_test) err_images = images_test[err] err_y_hat = y_hat_test[err] err_y = y_test[err] print('err_y_hat:\n', err_y_hat) print('err_y:\n', err_y) plt.figure(figsize=(15, 15), facecolor='w') for index, image in enumerate(err_images): if index >= 20: break plt.subplot(4, 5, index+1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('err:%i, real:%i' % (err_y_hat[index], err_y[index])) plt.suptitle('Digital Handwriting recognition:Classifier--%s' % classifier_type, fontsize=15) plt.tight_layout(rect=(0, 0, 1, 0.94)) plt.show()

相对来说，SVM和随机森林算法效果都已经不错，但随机森林表现的要好一点，分析可能是SVM还需要调参。