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November 20, 2017 06:59
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K-Nearest Neighbors with the MNIST Dataset
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import tensorflow as tf | |
import numpy as np | |
from tensorflow.examples.tutorials.mnist import input_data | |
import matplotlib.image as mp_image | |
import matplotlib.pyplot as mp_pyplot | |
mnist = input_data.read_data_sets("minst_data/", one_hot=True) | |
training_digits, training_lables = mnist.train.next_batch(5000) | |
test_digits, test_lables = mnist.train.next_batch(10) | |
training_digits_pl = tf.placeholder("float", [None, 784]) | |
test_digit_pl = tf.placeholder("float", [784]) | |
l1_distance = tf.abs(tf.add(training_digits_pl, tf.negative(test_digit_pl))) | |
distance = tf.reduce_sum(l1_distance, axis=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(test_digits)): | |
pixels = test_digits[i].reshape((28, 28)) | |
mp_pyplot.imshow(pixels, cmap='gray') | |
mp_pyplot.show() | |
nn_index = sess.run(pred, feed_dict={ | |
training_digits_pl: training_digits, | |
test_digit_pl: test_digits[i, :] | |
}) | |
match = True | |
if np.argmax(training_lables[nn_index]) != np.argmax(test_lables[i]): | |
match = False | |
print("match:", match,"\t", "prediction:", np.argmax( | |
training_lables[nn_index]), "true lable", np.argmax(test_lables[i])) |
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