16-Classification-KNearestNeighbor.py

import numpy as np
import tensorflow as tf
import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
train_pixels, train_list_values = mnist.train.next_batch(100)
test_pixels, test_list_of_values = mnist.test.next_batch(100)

train_pixel_tensor = tf.placeholder("float",[None, 784])
test_pixel_tensor = tf.placeholder("float",[784])

#cost funtion and optimization
distance = tf.reduce_sum(tf.abs(tf.add(train_pixel_tensor,tf.neg(test_pixel_tensor))),reduction_indices = 1)
pred = tf.arg_min(distance, 0)

#testing
accuracy = 0

init = tf.initialize_all_variables()
with tf.Session() as sess:
	sess.run(init)
	for i in range(len(test_list_of_values)):
		nn_index = sess.run(pred, feed_dict={train_pixel_tensor:train_pixels,test_pixel_tensor:test_pixels[i,:]})
		print("Test N° ", i,"Predicted Class: ", np.argmax(train_list_values[nn_index]), "True Class: ", np.argmax(test_list_of_values[i]))
		if np.argmax(train_list_values[nn_index]) == np.argmax(test_list_of_values[i]):
			accuracy += 1./len(test_pixels)
			print("Result = ", accuracy)