机器学习笔记之：使用Tensorflow实现基于L1的最近邻域分类器

# coding:utf-8
'''
最近邻域分类器
'''
import numpy as np
import tensorflow as tf
import os

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('D:/Develop/DL/deeplearning-notes/datas/mnist',one_hot=True)

X_train,Y_train = mnist.train.next_batch(10000)
X_test,Y_test = mnist.test.next_batch(2000)

xtrain = tf.placeholder(tf.float32,[None,784])
xtest = tf.placeholder(tf.float32,[784])

# 使用L1计算
distance = tf.reduce_sum(tf.abs(tf.add(xtrain,tf.negative(xtest))),axis=1)

# 预测
pred = tf.argmin(distance,0)

accuracy = 0.0

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    num_test = len(X_test)
    for i in range(num_test):
        nn_index = sess.run(pred,feed_dict={xtrain:X_train,xtest:X_test[i,:]})
        pred_class_label = np.argmax(Y_train[nn_index])
        true_class_label = np.argmax(Y_test[i])

        print('test:',i,'predicted class label:',pred_class_label,'true class label',true_class_label)

        if pred_class_label == true_class_label:
            accuracy += 1

    print('Done')
    accuracy /= num_test
    print('Accuracy:',accuracy)