How to do it...

  1. To start, we load the necessary libraries. We are also loading some PCA tools from sklearn so that we can change the resulting data from four dimensions to two dimensions for visualization purposes:
import numpy as np 
import matplotlib.pyplot as plt 
import tensorflow as tf 
from sklearn import datasets 
from scipy.spatial import cKDTree 
from sklearn.decomposition import PCA 
from sklearn.preprocessing import scale 
  1. We start a graph session, and load the iris dataset:
sess = tf.Session() 
iris = datasets.load_iris() 
num_pts = len(iris.data) 
num_feats = len(iris.data[0]) 
  1. We'll now set the groups, generations, and create the variables we need for the graph:
k=3 generations = 25 data_points = tf.Variable(iris.data) cluster_labels ...

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