04使用tensorflow实现knn算法，mnist数据集

用tensorflow实现knn算法，使用的mnist数据集

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 加载数据集
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# 生成数据集，用knn算法时会将数据全部加载到内存中，所以knn并不太适合大数据，此例子仅仅作演示
Xtr, Ytr = mnist.train.next_batch(5000)
Xte, Yte = mnist.test.next_batch(200)

# 定义占位节点
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])

# knn算法的实现
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
pred = tf.arg_min(distance, 0)

accuracy = 0.

init = tf.global_variables_initializer()

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

    for i in range(len(Xte)):

        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})

        print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
              "True Class:", np.argmax(Yte[i]))

        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
            accuracy += 1. / len(Xte)
    print("Done!")
    print("Accuracy:", accuracy)
# Accuracy: 0.9400000000000007