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3. Recompute the centroids using current cluster membership, such that the
sum of squared distances decreases
4. Repeat the last two steps until convergence is met
Chapter 10
In the previous TensorFlow versions the KMeans class was implemented in the
Contrib module; however, the class is no longer available in TensorFlow 2.0. Here
we will instead use the advanced mathematical functions provided in TensorFlow
2.0 to implement k-means clustering.
K-means in TensorFlow 2.0
To demonstrate k-means in TensorFlow, we will use randomly generated data in
the code that follows. Our randomly generated data will contain 200 samples, and
we will divide them into three clusters. We start with importing all the required
modules and defining the variables, determining the number of sample points
(points_n), the number of clusters to be formed (clusters_n), and the number of
iterations we will be doing (iteration_n). We also set the seed for random number
to ensure that our work is reproducible:
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
points_n = 200
clusters_n = 3
iteration_n = 100
seed = 123
np.random.seed(seed)
tf.random.set_seed(seed)
Now we randomly generate data and from the data select three centroids randomly:
points = np.random.uniform(0, 10, (points_n, 2))
centroids = tf.slice(tf.random.shuffle(points), [0, 0], [clusters_n,
-1])
You can see the scatter plot of all the points and the randomly selected three
centroids in the following graph:
plt.scatter(points[:, 0], points[:, 1], s=50, alpha=0.5)
plt.plot(centroids[:, 0], centroids[:, 1], 'kx', markersize=15)
plt.show()
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