计算机视觉（CV）sklearn之分类算法与手写数字识别

Posted 2022-10-31

【计算机视觉（CV）】sklearn之分类算法与手写数字识别

---

(文章目录)

---

前言

(一)、任务描述

• 数据介绍
• 数据读取与存储形式
• sklearn分类模型
• 代码实现与结果

---

(二)、环境配置

本实践代码运行的环境配置如下：Python版本为3.7，PaddlePaddle版本为2.0.0，操作平台为ＡI Studio。大部分深度学习项目都要经过以下几个过程：数据准备、模型配置、模型训练、模型评估。

import paddle
import numpy as np
import matplotlib.pyplot as plt
print(paddle.__version__)

# cpu/gpu环境选择，在 paddle.set_device() 输入对应运行设备。
# device = paddle.set_device(gpu)

---

一、数据集介绍

• Training set images: train-images-idx3-ubyte.gz (9.9 MB, 解压后 47 MB, 包含 60,000 个样本)
• Training set labels: train-labels-idx1-ubyte.gz (29 KB, 解压后 60 KB, 包含 60,000 个标签)
• Test set images: t10k-images-idx3-ubyte.gz (1.6 MB, 解压后 7.8 MB, 包含 10,000 个样本)
• Test set labels: t10k-labels-idx1-ubyte.gz (5KB, 解压后 10 KB, 包含 10,000 个标签)

---

二、数据读取与存储形式

!unzip data/data7869/mnist.zip

输出结果如下图1所示：

!gzip -dfq mnist/train-labels-idx1-ubyte.gz
!gzip -dfq mnist/t10k-labels-idx1-ubyte.gz
!gzip -dfq mnist/train-images-idx3-ubyte.gz
!gzip -dfq mnist/t10k-images-idx3-ubyte.gz

---

(一)、导入相关包

首先我们引入本案例需要的所有模块

import struct,os
import numpy as np
from array import array as pyarray
from numpy import append, array, int8, uint8, zeros
import matplotlib.pyplot as plt

%matplotlib inline 

---

(二)、参数配置

def load_mnist(image_file, label_file, path="mnist"):
    digits=np.arange(10)

    fname_image = os.path.join(path, image_file)
    fname_label = os.path.join(path, label_file)

    flbl = open(fname_label, rb)
    magic_nr, size = struct.unpack(">II", flbl.read(8))
    lbl = pyarray("b", flbl.read())
    flbl.close()

    fimg = open(fname_image, rb)
    magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16))
    img = pyarray("B", fimg.read())
    fimg.close()

    ind = [ k for k in range(size) if lbl[k] in digits ]
    N = len(ind)

    images = zeros((N, rows*cols), dtype=uint8)
    labels = zeros((N, 1), dtype=int8)
    for i in range(len(ind)):
        images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((1, rows*cols))
        labels[i] = lbl[ind[i]]

    return images, labels


train_image, train_label = load_mnist("train-images-idx3-ubyte", "train-labels-idx1-ubyte")
test_image, test_label = load_mnist("t10k-images-idx3-ubyte", "t10k-labels-idx1-ubyte")

---

(三)、数据展示

import matplotlib.pyplot as plt

def show_image(imgdata,imgtarget,show_column, show_row):
    #注意这里的show_column*show_row==len(imgdata)
    for index,(im,it) in enumerate(list(zip(imgdata,imgtarget))):
        xx = im.reshape(28,28)
        plt.subplots_adjust(left=1, bottom=None, right=3, top=2, wspace=None, hspace=None)
        plt.subplot(show_row, show_column, index+1)
        plt.axis(off)
        plt.imshow(xx , cmap=gray,interpolation=nearest)
        plt.title(label:%i % it)
# 显示训练集前50数字
show_image(train_image[:50], train_label[:50], 10,5)

输出结果如下图2所示：

---

三、sklearn 分类模型

本文使用的sklearn分类模型有：

• K-Nearest Neighbor Classification
• Naive Bayes
• Decision Tree
• Random Forest
• Logistic Regression
• Support Vector Machine

https://blog.csdn.net/u013909139/article/details/69740089

http://cuijiahua.com/blog/2017/11/ml_8_svm_1.html
http://cuijiahua.com/blog/2017/11/ml_9_svm_2.html

---

数据归一化

https://blog.csdn.net/program_developer/article/details/78637711

此处采用min-max标准化将数据进行归一化操作。 image = [im/255.0 for im in image]

---

train_image=[im/255.0 for im in train_image]
test_image=[im/255.0 for im in test_image]

---

四、sklearn模型分类

---

(一)、K-Nearest Neighbor Classification

print(len(train_image))
print(len(train_label))

输出结果如下图3所示：

from sklearn.metrics import accuracy_score,classification_report
from sklearn.neighbors import KNeighborsClassifier

knc = KNeighborsClassifier(n_neighbors=10)
knc.fit(train_image,train_label.ravel())

输出结果如下图4所示：

predict = knc.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))

输出结果如下图5所示：

print("Classification report for classifier %s:\\n%s\\n" % (knc, classification_report(test_label, predict)))

输出结果如下图6所示：

---

(二)、Naive Bayes

from sklearn.naive_bayes import MultinomialNB

mnb = MultinomialNB()
mnb.fit(train_image,train_label)
predict = mnb.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))
print("Classification report for classifier %s:\\n%s\\n" % (mnb, classification_report(test_label, predict)))

输出结果如下图7所示：

---

(三)、Decision Tree

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()
dtc.fit(train_image,train_label)
predict = dtc.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))
print("Classification report for classifier %s:\\n%s\\n" % (dtc, classification_report(test_label, predict)))

输出结果如下图8所示：

---

(四)、Random Forest

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier()
rfc.fit(train_image,train_label)
predict = rfc.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))
print("Classification report for classifier %s:\\n%s\\n" % (rfc, classification_report(test_label, predict)))

---

(五)、Logistic Regression

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()
lr.fit(train_image,train_label)
predict = lr.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))
print("Classification report for classifier %s:\\n%s\\n" % (lr, classification_report(test_label, predict)))

输出结果如下图9所示：

---

(六)、Support Vector Machine

from sklearn.svm import SVC

svc = SVC()
svc.fit(train_image,train_label)
predict = svc.predict(test_image)
print("accuracy_score: %.4lf" % accuracy_score(predict,test_label))
print("Classification report for classifier %s:\\n%s\\n" % (svc, classification_report(test_label, predict)))

各分类模型准确度比较：

模型	准确度
K-Nearest Neighbor Classification	0.9665
Naive Bayes	0.8375
Decision Tree	0.8758
Random Forest	0.9486
Logistic Regression	0.9202
Support Vector Machine	0.9769

我们可以将预测结果展示出来：

import matplotlib.pyplot as plt

def show_image(imgtestdata, imgtesttarget, show_column, show_row):
    #注意这里的show_column*show_row==len(imgtestdata)
    for index,(im,it) in enumerate(list(zip(imgtestdata, imgtesttarget))):
        xx = im.reshape(28,28)
        plt.subplots_adjust(left=1, bottom=None, right=3, top=4, wspace=None, hspace=None)
        plt.subplot(show_row, show_column, index+1)
        plt.axis(off)
        plt.imshow(xx , cmap=gray,interpolation=nearest)
        plt.title(predict:%i % it)

show_image(test_image[:100], predict, 10, 10)

部分输出结果如下图10所示：

---

总结

本系列文章内容为根据清华社出版的《自然语言处理实践》所作的相关笔记和感悟，其中代码均为基于百度飞桨开发，若有任何侵权和不妥之处，请私信于我，定积极配合处理，看到必回！！！

最后，引用本次活动的一句话，来作为文章的结语～(￣▽￣～)~：

【**学习的最大理由是想摆脱平庸，早一天就多一份人生的精彩；迟一天就多一天平庸的困扰。**】