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digit-recognizer

README.md

数字识别

比赛说明

  • MNIST（"修改后的国家标准与技术研究所"）是计算机视觉的事实上的 "hello world" 数据集。自1999年发布以来，手写图像的经典数据集已成为基准分类算法的基础。随着新机器学习技术的出现，MNIST 仍然是研究人员和学习者的可靠资源。
  • 在本次比赛中，您的目标是正确 识别数以万计手写图像的数字 。我们策划了一套教程式的内核，涵盖从回归到神经网络的一切。我们鼓励您尝试使用不同的算法来学习第一手什么是有效的，以及技术如何比较。

注意：项目规范

成员角色

角色 用户 内容 代码
负责: knn 诺木人 knn项目文档 knn项目代码
负责: svm 马小穆 svm项目文档 svm项目代码
负责: 随机森林 平淡的天 随机森林项目文档 随机森林项目代码
负责: 神经网络 平淡的天 神经网络项目文档 神经网络项目代码
负责: cnn == cnn项目文档 cnn项目代码

数字识别 第一期(2018-04-18)

组长 组员 组员 组员 组员 组员 组员
限定心态 strengthen VS53MV 不会修电脑 远心 小耀哥_0011 丨

数字识别 第二期(2018-04-21)

组长 组员 组员 组员 组员 组员
凌少skier [Blue] [Max] [考拉] [Happyorg] [过客]

数字识别 第三期(2018-05-03)

负责人 组员 组员
技术负责人-诺木人
辅助负责人-平淡的心
辅助负责人-张凯
活动发起人-片刻		 ifeng
draw
Faith
ggggggggo
嘿！漆漆
kickfilper
Lucien Chen
LQW
琴剑蓝天
時間ｄāń漠
歲寒✅已认证
給力小青年
星尘
 瑛瑛wang
有一个人很酷
静水流深
♡稳稳的幸福
Verestràsz
vslyu
:)
菠菜
QQ小冰
浅紫色
R
ROOT

数字识别 第四期(2018-05-08)

负责人 组员 组员 组员
技术负责人-诺木人
辅助负责人-BrianCai
辅助负责人-嘿！漆漆
 兰博归来
柳生
ZARD Forever
你别理我我没网
666
黄蛟
冬冬
荼蘼
烁今		 简雨
B0lt1st
nickine
dying in the sun
[王琪琪]
[常想一二]
[以朱代墨]
Mang0
[TonyZERO]
 [冰花小子]
[阿铮]
[zh哲]
[小菜鸡]
[电酱prpr]
[琉璃火]
[张假飞]
HAN Shuai
[有人@我]
天儿
Jaybo

开发流程（knn项目案例）

收集数据: 提供文本文件(目标变量+数据特征)

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准备数据: 分离数据特征和目标变量，加载测试数据

# 加载数据
def opencsv():
    # 使用 pandas 打开
    data = pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')
    data1 = pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')

    train_data = data.values[0:, 1:]  # 读入全部训练数据
    train_label = data.values[0:, 0]
    test_data = data1.values[0:, 0:]  # 测试全部测试个数据
    return train_data, train_label, test_data

trainData, trainLabel, testData = opencsv()

模型训练: 产生训练模型

# 模型训练
def knnClassify(trainData, trainLabel):
    knnClf = KNeighborsClassifier()   # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
    knnClf.fit(trainData, ravel(trainLabel))
    return knnClf

knnClf = knnClassify(trainData, trainLabel)

模型评估: 用于评估结果的正确率和召回率

暂时没写，后面会进行优化

结果预测: 通过模型来预测新来数据的结果

# 结果预测
testLabel = knnClf.predict(testData)

结果导出

def saveResult(result, csvName):
    with open(csvName, 'wb') as myFile:
        myWriter = csv.writer(myFile)
        myWriter.writerow(["ImageId", "Label"])
        index = 0
        for i in result:
            tmp = []
            index = index+1
            tmp.append(index)
            # tmp.append(i)
            tmp.append(int(i))
            myWriter.writerow(tmp)

# 结果的输出
saveResult(testLabel, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_knn.csv')