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January 28, 2017 15:12
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from __future__ import print_function | |
import numpy as np | |
import tensorflow as tf | |
# Import MNIST data | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) | |
# In this example, we limit mnist data | |
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates) | |
Xte, Yte = mnist.test.next_batch(200) #200 for testing | |
# tf Graph Input | |
xtr = tf.placeholder("float", [None, 784]) | |
xte = tf.placeholder("float", [784]) | |
# Nearest Neighbor calculation using L1 Distance | |
# Calculate L1 Distance | |
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1) | |
# Prediction: Get min distance index (Nearest neighbor) | |
pred = tf.arg_min(distance, 0) | |
accuracy = 0. | |
# Initializing the variables | |
init = tf.global_variables_initializer() | |
# Launch the graph | |
with tf.Session() as sess: | |
sess.run(init) | |
# loop over test data | |
for i in range(len(Xte)): | |
# Get nearest neighbor | |
nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]}) | |
# Get nearest neighbor class label and compare it to its true label | |
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) | |
wp_embed_register_handler( 'gist', '/https?:\/\/gist\.github\.com\/([a-z0-9]+)(\?file=.*)?/i', 'bhww_embed_handler_gist' ); | |
function bhww_embed_handler_gist( $matches, $attr, $url, $rawattr ) { | |
$embed = sprintf( | |
'<script src="https://gist.github.com/%1$s.js%2$s"></script>', | |
esc_attr($matches[1]), | |
esc_attr($matches[2]) | |
); | |
return apply_filters( 'embed_gist', $embed, $matches, $attr, $url, $rawattr ); | |
} |
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