KNN实现手写数字识别
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1 - 导入模块
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from ld_mnist import load_digits
%matplotlib inline2 - 导入数据及数据预处理
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
def load_digits():
mnist = input_data.read_data_sets("path/", one_hot=True)
return mnist
mnist = load_digits()Extracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\train-images-idx3-ubyte.gz
Extracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\train-labels-idx1-ubyte.gz
Extracting C:/Users/marsggbo/Documents/Code/ML/TF Tutorial/data/MNIST_data\t10k-images-idx3-ubyte.gz
Extracting 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))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 = 6
for i in range(1,nums+1):
plt.subplot(1,nums,i)
plt.imshow(x_train[i].reshape(28,28), cmap="gray")
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))
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):
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_hatclf = 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准确率略高。
MARSGGBO原创
2017-8-21
