KNN implements MNIST digital classification

import torch
from torch.utils.data import DataLoader
import torchvision.datasets as dsets
import numpy as np
import operator

batch_size = 100
data_path = './'

def KNN_distance(k, dis, trains_1, labels_2, test):

    #曼哈顿距离（Manhattan distance）： 简称M
    #欧式距离（Euclidean Metric）： 简称E
    assert  dis == 'M' or dis == 'E'
    count = test.shape[0]
    label_list = []

    # 欧式距离 sqrt((x1-x2)^2 + (y1-y2)^2)
    if dis == 'E':
        for i in range(count):
            distance = np.sqrt(np.sum(((trains_1 - np.tile(test[i], (trains_1.shape[0], 1))) ** 2), axis=1))
            nearest_k = np.argsort(distance)
            topK = nearest_k[:k]
            classCount = {
    
    }
            for i in topK:
                # print(i)
                classCount[labels_2[i]] = classCount.get(labels_2[i], 0) + 1
            sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
            label_list.append(sortedClassCount[0][0])
    # 曼哈顿距离：|x1-x2| + |y1-y2|
    elif dis == 'M':
        for i in range(count):
            distance = np.sum(np.abs(trains_1 - np.tile(test[i], (trains_1.shape[0], 1))), axis=1)

            nearest_k = np.argsort(distance)
            topK = nearest_k[:k]
            classCount = {
    
    }
            for i in topK:
                # print(i)
                classCount[labels_2[i]] = classCount.get(labels_2[i], 0) + 1
            sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
            label_list.append(sortedClassCount[0][0])
    return label_list

def GetXMean(img_data):
    """
    calculate the image's mean and std value
    :param img_data: images
    :return: mean, std
    """
    mean = []
    std = []
    for i, img in enumerate(img_data):
        mean.append(img[:, :].mean())
        std.append(img[:, :].std())
    mean = (np.array(mean) + 0.5).astype(np.int32)
    std = (np.array(std) + 0.5).astype(np.int32)
    return mean, std

def Centralized(img_data, mean, std):
    return img_data.astype(np.int32) - mean.reshape((mean.shape[0], 1, 1))


# download MNIST dataset
train_dataset = dsets.MNIST(root=data_path,
                            train=True,
                            transform=None,
                            download=True)

test_dataset = dsets.MNIST(root=data_path,
                            train=False,
                            transform=None,
                            download=True)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True) #数据打乱

test_loader = DataLoader(dataset=test_dataset,
                         batch_size=batch_size,
                         shuffle=True) #数据打乱

# 训练集样本数和维度
print("train_data: ", train_dataset.train_data.size())
# 训练集标签的长度
print("train_data: ", train_dataset.train_labels.size())
# 测试集样本数和维度
print("test_data: ", test_dataset.train_data.size())
# 测试集标签的长度
print("test_data: ", test_dataset.train_labels.size())

if __name__ == '__main__':
    x_train = train_loader.dataset.train_data.numpy()
    x_mean, x_std = GetXMean(x_train)
    x_train = Centralized(x_train, x_mean, x_std).reshape(x_train.shape[0], 28*28)
    y_train = train_loader.dataset.train_labels.numpy()

    x_test = test_loader.dataset.test_data[:1000].numpy()
    x_mean, x_std = GetXMean(x_test)
    x_test = Centralized(x_test, x_mean, x_std).reshape(x_test.shape[0], 28*28)
    y_test = test_loader.dataset.test_labels[:1000].numpy()
    num_test = y_test.shape[0]

    y_test_pred = KNN_distance(5, 'E', x_train, y_train, x_test)
    num_correct = np.sum(y_test_pred == y_test)
    accuracy = float(num_correct) / num_test
    print('Euclidean: Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))


    y_test_pred = KNN_distance(5, 'M', x_train, y_train, x_test)
    num_correct = np.sum(y_test_pred == y_test)
    accuracy = float(num_correct) / num_test
    print('Manhattan: Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))

"""
计算输出结果：
train_data:  torch.Size([60000, 28, 28])
train_data:  torch.Size([60000])
test_data:  torch.Size([10000, 28, 28])
test_data:  torch.Size([10000])
Euclidean: Got 964 / 1000 correct => accuracy: 0.964000
Manhattan: Got 942 / 1000 correct => accuracy: 0.942000
"""

It can be seen from the above calculation results that using Manhattan distance and Euclidean distance to implement the KNN algorithm respectively, the results obtained have a certain error. The fundamental reason is that the method of calculating the distance is different. For details, see Deep Learning-KNN Algorithm ( k-nearest neighbor algorithm) .
Before performing the KNN algorithm, certain pre-processing is performed on the image, that is, removing the mean value, normalizing, etc., all in order to improve the accuracy of the inference.