如何使用LinearSVM训练数据集？

Question

我目前是机器学习的新手，想问一下如何训练数据集。我的数据集是一个包含字母图像的文件夹。这是我正在处理的代码，目前卡在火车上。

import numpy as np
from sklearn.svm import LinearSVC
import os
import cv2
import joblib

Dataset_folder = 'Dataset/'

directory = os.listdir(Dataset_folder)
folders = directory

folders =  sorted(folders)

print (folders)

trainset = []

for folder in directory:
    flist = os.listdir(os.path.join(Dataset_folder, folder))
    for f in flist:
        img = cv2.imread(Dataset_folder + folder + "/" + f,0)
        img = cv2.resize(img, (36,36))
        trainset.append(img)
print ('done')
# Labeling for trainset
train_label = []
for i in range(0,10):
    temp = 140*[i]
    train_label += temp

trainset = np.reshape(trainset, (4650, -1))

# Create an linear SVM object
clf = LinearSVC()

# Perform the training
clf.fit(trainset, train_label)
print("Training finished successfully")