from __future__ import print_function
import numpy as np
import tensorflow as tf
# 导入 MNIST 数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# 这个样例中我们只用部分数据
Xtr, Ytr = mnist.train.next_batch(5000) #5000 来训练 (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 来训练
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# 最近邻的计算用L1的距离
#计算 L1 的距离
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
# 预计: 得到最小距离下标 (最近邻)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# loop over test data
for i in range(len(Xte)):
# 得到最近邻
nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
# 比较预测和真实值
print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
"True Class:", np.argmax(Yte[i]))
# Calculate accuracy
if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
accuracy += 1./len(Xte)
print("Done!")
print("Accuracy:", accuracy)
