tensorflow基础模型之KNN（最邻近值）算法

KNN算法原理，本文将用tensorflow使用KNN算法训练MINST数据集。

Codes：

from __future__ import print_function, division
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import numpy as np
import tensorflow as tf
# 导入MNIST数据
from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
​
# 取5000训练数据和200测试数据
Xtr, Ytr = mnist.train.next_batch(5000)  # 5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200)  # 200 for testing
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# 图输入
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
​
# 使用L1距离获取最邻近值
# 计算L1距离
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
# 预测：获取最小距离（最邻近值）
pred = tf.arg_min(distance, 0)
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accuracy = 0.
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# 初始化参数
init = tf.global_variables_initializer()
​
# 开始训练
with tf.Session() as sess:
   
   sess.run(init)
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   # 测试
   for i in range(len(Xte)):
       # 获取最邻近
       nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
       # 预测值与实际值对比
       print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]),
             "True Class:", np.argmax(Yte[i]))
       # 计算准确率
       if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
           accuracy += 1. / len(Xte)
   print("Done!")
   print("Accuracy:", accuracy)
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