使用tf+knn实现手写数字识别

# 1 重要
# 2 KNN CNN 2种
# 3 样本 
# 4 旧瓶装新酒 ：数字识别的不同
# 4.1 网络 4。2 每一级 4.3 先原理 后代码 
# 本质：knn test 样本 K个 max4 3个1 -》1

# 1 load Data  1.1 随机数 1.2 4组 训练 测试 （图片 和 标签）
# 2 knn test train distance 5*500 = 2500 784=28*28
# 3 knn k个最近的图片5 500 1-》500train （4）
# 4 k个最近的图片-> parse centent label
# 5 label -》 数字 p9 测试图片-》数据
# 6 检测概率统计
import tensorflow as tf
import numpy as np
import random 
from tensorflow.examples.tutorials.mnist import input_data
# load data 2 one_hot : 1 0000 1 fileName 
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)
# 属性设置
trainNum = 55000
testNum = 10000
trainSize = 500
testSize = 5
k = 4
# data 分解 1 trainSize   2范围0-trainNum 3 replace=False 
trainIndex = np.random.choice(trainNum,trainSize,replace=False)
testIndex = np.random.choice(testNum,testSize,replace=False)
trainData = mnist.train.images[trainIndex]# 训练图片
trainLabel = mnist.train.labels[trainIndex]# 训练标签
testData = mnist.test.images[testIndex]
testLabel = mnist.test.labels[testIndex]
# 28*28 = 784
print('trainData.shape=',trainData.shape)#500*784 1 图片个数 2 784?
print('trainLabel.shape=',trainLabel.shape)#500*10
print('testData.shape=',testData.shape)#5*784
print('testLabel.shape=',testLabel.shape)#5*10
print('testLabel=',testLabel)# 4 :testData [0]  3:testData[1] 6 
# tf input  784->image
trainDataInput = tf.placeholder(shape=[None,784],dtype=tf.float32)
trainLabelInput = tf.placeholder(shape=[None,10],dtype=tf.float32)
testDataInput = tf.placeholder(shape=[None,784],dtype=tf.float32)
testLabelInput = tf.placeholder(shape=[None,10],dtype=tf.float32)
#knn distance 5*785.  5*1*784
# 5 500 784 (3D) 2500*784
f1 = tf.expand_dims(testDataInput,1) # 维度扩展
f2 = tf.subtract(trainDataInput,f1)# 784 sum(784)
f3 = tf.reduce_sum(tf.abs(f2),reduction_indices=2)# 完成数据累加 784 abs
# 5*500
f4 = tf.negative(f3)# 取反
f5,f6 = tf.nn.top_k(f4,k=4) # 选取f4 最大的四个值
# f3 最小的四个值
# f6 index->trainLabelInput
f7 = tf.gather(trainLabelInput,f6)
# f8 num reduce_sum  reduction_indices=1 '竖直'
f8 = tf.reduce_sum(f7,reduction_indices=1)
# tf.argmax 选取在某一个最大的值 index
f9 = tf.argmax(f8,dimension=1)

# f9 -> test5 image -> 5 num
with tf.Session() as sess:
    # f1 <- testData 5张图片
    p1 = sess.run(f1,feed_dict={testDataInput:testData[0:5]})
    print('p1=',p1.shape)# p1= (5, 1, 784)
    p2 = sess.run(f2,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5]})
    print('p2=',p2.shape)#p2= (5, 500, 784) (1,100)  
    p3 = sess.run(f3,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5]})
    print('p3=',p3.shape)#p3= (5, 500)
    print('p3[0,0]=',p3[0,0]) #130.451 knn distance p3[0,0]= 155.812
    
    p4 = sess.run(f4,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5]})
    print('p4=',p4.shape)
    print('p4[0,0]',p4[0,0])
    
    p5,p6 = sess.run((f5,f6),feed_dict={trainDataInput:trainData,testDataInput:testData[0:5]})
    #p5= (5, 4) 每一张测试图片（5张）分别对应4张最近训练图片
    #p6= (5, 4)
    print('p5=',p5.shape)
    print('p6=',p6.shape)
    print('p5[0,0]',p5[0])
    print('p6[0,0]',p6[0])# p6 index
    
    p7 = sess.run(f7,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5],trainLabelInput:trainLabel})
    print('p7=',p7.shape)#p7= (5, 4, 10)
    print('p7[]',p7)
    
    p8 = sess.run(f8,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5],trainLabelInput:trainLabel})
    print('p8=',p8.shape)
    print('p8[]=',p8)
    
    p9 = sess.run(f9,feed_dict={trainDataInput:trainData,testDataInput:testData[0:5],trainLabelInput:trainLabel})
    print('p9=',p9.shape)
    print('p9[]=',p9)
    
    p10 = np.argmax(testLabel[0:5],axis=1)
    print('p10[]=',p10)
j = 0
for i in range(0,5):
    if p10[i] == p9[i]:
        j = j+1
print('ac=',j*100/5)