December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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Getting ready
In order to implement SOM, let's first understand how it works. As the first step, the weights of the network are initialized to either some random value or by taking random samples from the input. Each neuron occupying a space in the lattice will be assigned specific locations. Now as an input is presented, the neuron with the least distance from the input is declared Winner (WTU). This is done by measuring the distance between weight vector (W) and input vectors (X) of all neurons:
Here, dj is the distance of weights of neuron j from input X. The neuron with minimum d value is the winner.
Next, the weights of the winning neuron ...
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