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Created September 30, 2017 18:28
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k-means in Tensorflow
import tensorflow as tf
import numpy as np
import time
MAX_ITERS = 1000
start = time.time()
points = tf.Variable(tf.random_uniform([N,2]))
cluster_assignments = tf.Variable(tf.zeros([N], dtype=tf.int64))
# Silly initialization: Use the first K points as the starting
# centroids. In the real world, do this better.
centroids = tf.Variable(tf.slice(points.initialized_value(), [0,0], [K,2]))
# Replicate to N copies of each centroid and K copies of each
# point, then subtract and compute the sum of squared distances.
rep_centroids = tf.reshape(tf.tile(centroids, [N, 1]), [N, K, 2])
rep_points = tf.reshape(tf.tile(points, [1, K]), [N, K, 2])
sum_squares = tf.reduce_sum(tf.square(rep_points - rep_centroids),
# Use argmin to select the lowest-distance point
best_centroids = tf.argmin(sum_squares, 1)
did_assignments_change = tf.reduce_any(tf.not_equal(best_centroids,
def bucket_mean(data, bucket_ids, num_buckets):
total = tf.unsorted_segment_sum(data, bucket_ids, num_buckets)
count = tf.unsorted_segment_sum(tf.ones_like(data), bucket_ids, num_buckets)
return total / count
means = bucket_mean(points, best_centroids, K)
# Do not write to the assigned clusters variable until after
# computing whether the assignments have changed - hence with_dependencies
with tf.control_dependencies([did_assignments_change]):
do_updates =
init = tf.initialize_all_variables()
sess = tf.Session()
changed = True
iters = 0
while changed and iters < MAX_ITERS:
iters += 1
[changed, _] =[did_assignments_change, do_updates])
[centers, assignments] =[centroids, cluster_assignments])
end = time.time()
print ("Found in %.2f seconds" % (end-start)), iters, "iterations"
print "Centroids:"
print centers
print "Cluster assignments:", assignments
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