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Chapter 10
Colour mapping using SOM
Some of the interesting properties of the feature map of the input space generated
by SOM are:
• The feature map provides a good representation of the input space. This
property can be used to perform vector quantization so that we may have
a continuous input space, and using SOM we can represent it in a discrete
output space.
• The feature map is topologically ordered, that is, the spatial location of a
neuron in the output lattice corresponds to a particular feature of the input.
• The feature map also reflects the statistical distribution of the input space;
the domain that has the largest number of input samples gets a wider area
in the feature map.
These features of SOM make them the natural choice for many interesting
applications. Here we use SOM for clustering a range of given R, G, and B pixel
values to a corresponding color map. We start with the importing of modules:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
The main component of the code is our class WTU. The class __init__ function
initializes various hyperparameters of our SOM, the dimensions of our 2D lattice
(m, n), the number of features in the input (dim), the neighborhood radius (sigma),
the initial weights, and the topographic information:
# Define the Winner Take All units
class WTU(object):
#_learned = False
def __init__(self, m, n, dim, num_iterations, eta = 0.5, sigma =
None):
"""
m x n : The dimension of 2D lattice in which neurons
are arranged
dim : Dimension of input training data
num_iterations: Total number of training iterations
eta : Learning rate
sigma: The radius of neighbourhood function.
"""
self._m = m
self._n = n