Python实现knn算法手写数字识别

KNN实现手写数字识别

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1 - 导入模块

import numpy as npimport matplotlib.pyplot as pltfrom PIL import  Image%matplotlib inline

2 - 导入数据及数据预处理

因为我下载的mnist数据是*.gz格式的，所以为了方便读取数据就是用了TensorFlow提供的模块。

import tensorflow as tf# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datadef load_digits():    mnist = input_data.read_data_sets("path/", one_hot=True)    return mnistmnist = load_digits()

输出结果

Extracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\train-images-idx3-ubyte.gzExtracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\train-labels-idx1-ubyte.gzExtracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\t10k-images-idx3-ubyte.gzExtracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\t10k-labels-idx1-ubyte.gz

数据维度

print("Train: "+ str(mnist.train.images.shape))print("Train: "+ str(mnist.train.labels.shape))print("Test: "+ str(mnist.test.images.shape))print("Test: "+ str(mnist.test.labels.shape))

输出结果

Train: (55000, 784)Train: (55000, 10)Test: (10000, 784)Test: (10000, 10)

mnist数据采用的是TensorFlow的一个函数进行读取的，由上面的结果可以知道训练集数据X_train有55000个，每个X的数据长度是784（28*28）。

x_train, y_train, x_test, y_test = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

展示手写数字

nums = 6for i in range(1,nums+1):    plt.subplot(1,nums,i)    plt.imshow(x_train[i].reshape(28,28), cmap="gray")

输出结果

3 - 构建模型

class Knn():    def __init__(self,k):        self.k = k        self.distance = {}    def topKDistance(self, x_train, x_test):        '''        计算距离，这里采用欧氏距离        '''            print("计算距离...")        distance = {}        for i in range(x_test.shape[0]):            dis1 = x_train - x_test[i]            dis2 = np.sqrt(np.sum(dis1*dis1, axis=1))            # 取最近的k个索引            distance[str(i)] = np.argsort(dis2)[:self.k]            if i%1000==0:                print(distance[str(i)])        return distance    def predict(self, x_train, y_train, x_test):        '''        预测        '''        self.distance = self.topKDistance(x_train, x_test)        y_hat = []        print("选出每项最佳预测结果")        for i in range(x_test.shape[0]):            classes = {}            for j in range(self.k):                # 找出前k个元素中相同元素最多的一个                num = np.argmax(y_train[self.distance[str(i)][j]])                classes[num] = classes.get(num, 0) + 1            sortClasses = sorted(classes.items(), key= lambda x:x[1], reverse=True)            y_hat.append(sortClasses[0][0])        y_hat = np.array(y_hat).reshape(-1,1)        return y_hat    def fit(self, x_train, y_train, x_test, y_test):        '''        计算准确率        '''        print("预测...")        y_hat = self.predict(x_train, y_train, x_test)#         index_hat  =np.argmax(y_hat , axis=1)        print("计算准确率...")        index_test = np.argmax(y_test, axis=1).reshape(1,-1)        accuracy = np.sum(y_hat.reshape(index_test.shape) == index_test)*1.0/y_test.shape[0]        return accuracy, y_hat

clf = Knn(10)accuracy, y_hat = clf.fit(x_train,y_train,x_test,y_test)print(accuracy)

预测...计算距离...[48843 33620 11186 22059 42003  9563 39566 10260 35368 31395][54214  4002 11005 15264 49069  8791 38147 47304 51494 11053][46624 10708 22134 20108 48606 19774  7855 43740 51345  9308][ 8758 47844 50994 45610  1930  3312 30140 17618   910 51918][14953  1156 50024 26833 26006 38112 31080  9066 32112 41846][45824 14234 48282 28432 50966 22786 40902 52264 38552 44080][24878  4655 20258 36065 30755 15075 35584 12152  4683 43255][48891 20744 47822 53511 54545 27392 10240  3970 25721 30357][  673 17747 33803 20960 25463 35723   969 50577 36714 35719][ 8255 42067 53282 14383 14073 52083  7233  8199  8963 12617]选出每项最佳预测结果计算准确率...0.9672

准确率好像还可以吼。