1. Neural Networks Lecture 17: Self-Organizing Maps
About Assignment #3
Two approaches to backpropagation learning:
1. “Per-pattern” learning:
Update weights after every exemplar presentation.
2. “Per-epoch” (batch-mode) learning:
Update weights after every epoch. During epoch, compute the sum of required changes for each weight across all exemplars. After epoch, update each weight using the respective sum.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

2. Neural Networks Lecture 17: Self-Organizing Maps
About Assignment #3
Per-pattern learning often approaches near-optimal network error quickly, but may then take longer to reach the error minimum.
During per-pattern learning, it is important to present the exemplars in random order.
Reducing the learning rate between epochs usually leads to better results.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

3. Neural Networks Lecture 17: Self-Organizing Maps
About Assignment #3
Per-epoch learning involves less frequent weight updates, which makes the initial approach to the error minimum rather slow.
However, per-epoch learning computes the actual network error and its gradient for each weight so that the network can make more informed decisions about weight updates.
Two of the most effective algorithms that exploit this information are Quickprop and Rprop.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

4. The Quickprop Learning Algorithm
The assumption underlying Quickprop is that the network error as a function of each individual weight can be approximated by a paraboloid.
Based on this assumption, whenever we find that the gradient for a given weight switched its sign between successive epochs, we should fit a paraboloid through these data points and use its minimum as the next weight value.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

5. The Quickprop Learning Algorithm
Illustration (sorry for the crummy paraboloid):
assumed error function (paraboloid) w E
slope: E’(t-1)
E(t-1)
w(t-1)
slope: E’(t)
E(t)
w(t)
w(t+1)
w(t-1)
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

6. The Quickprop Learning Algorithm
Newton’s method:
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

7. The Quickprop Learning Algorithm
For the minimum of E we must have:
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

8. The Quickprop Learning Algorithm
Notice that this method cannot be applied if the error gradient has not decreased in magnitude and has not changed its sign at the preceding time step.
In that case, we would ascent in the error function or make an infinitely large weight modification.
In most cases, Quickprop converges several times faster than standard backpropagation learning.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

9. Resilient Backpropagation (Rprop)
The Rprop algorithm takes a very different approach to improving backpropagation as compared to Quickprop.
Instead of making more use of gradient information for better weight updates, Rprop only uses the sign of the gradient, because its size can be a poor and noisy estimator of required weight updates.
Furthermore, Rprop assumes that different weights need different step sizes for updates, which vary throughout the learning process.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

10. Resilient Backpropagation (Rprop)
The basic idea is that if the error gradient for a given weight wij had the same sign in two consecutive epochs, we increase its step size ij, because the weight’s optimal value may be far away.
If, on the other hand, the sign switched, we decrease the step size.
Weights are always changed by adding or subtracting the current step size, regardless of the absolute value of the gradient.
This way we do not “get stuck” with extreme weights that are hard to change because of the shallow slope in the sigmoid function.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

11. Resilient Backpropagation (Rprop)
Formally, the step size update rules are:
Empirically, best results were obtained with initial step sizes of 0.1, +=1.2, -=1.2, max=50, and min=10-6.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

12. Resilient Backpropagation (Rprop)
Weight updates are then performed as follows:
It is important to remember that, like in Quickprop, in Rprop the gradient needs to be computed across all samples (per-epoch learning).
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

13. Resilient Backpropagation (Rprop)
The performance of Rprop is comparable to Quickprop; it also considerably accelerates backpropagation learning.
Compared to both the standard backpropagation algorithm and Quickprop, Rprop has one advantage:
Rprop does not require the user to estimate or empirically determine a step size parameter and its change over time.
Rprop will determine appropriate step size values by itself and can thus be applied “as is” to a variety of problems without significant loss of efficiency.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

14. The Counterpropagation Network
Let us look at the CPN structure again.
How can this network determine its hidden-layer winner unit? Y1 Y2
Output layer H1 H2 H3
Hidden layer X1 X2
Input layer
Additional connections!
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

15. Neural Networks Lecture 17: Self-Organizing Maps
The Solution: Maxnet
A maxnet is a recurrent, one-layer network that uses competition to determine which of its nodes has the greatest initial input value.
All pairs of nodes have inhibitory connections with the same weight -, where typically   1/(# nodes).
In addition, each node has a self-excitatory connection to itself, whose weight  is typically 1.
The nodes update their net input and their output by the following equations:
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

16. Neural Networks Lecture 17: Self-Organizing Maps
Maxnet
All nodes update their output simultaneously.
With each iteration, the neurons’ activations will decrease until only one neuron remains active.
This is the “winner” neuron that had the greatest initial input.
Maxnet is a biologically plausible implementation of a maximum-finding function.
In parallel hardware, it can be more efficient than a corresponding serial function.
We can add maxnet connections to the hidden layer of a CPN to find the winner neuron.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

17. Neural Networks Lecture 17: Self-Organizing Maps
Maxnet Example
Example of a Maxnet with five neurons and  = 1,  = 0.2:
0.5
0.24
0.07
0.9
0.9
0.24
0.07
Winner!
0.07
0.24
0.9 1
0.22
0.17
0.36
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

18. Self-Organizing Maps (Kohonen Maps)
As you may remember, the counterpropagation network employs a combination of supervised and unsupervised learning.
We will now study Self-Organizing Maps (SOMs) as examples for completely unsupervised learning (Kohonen, 1980).
This type of artificial neural network is particularly similar to biological systems (as far as we understand them).
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

19. Self-Organizing Maps (Kohonen Maps)
In the human cortex, multi-dimensional sensory input spaces (e.g., visual input, tactile input) are represented by two-dimensional maps.
The projection from sensory inputs onto such maps is topology conserving.
This means that neighboring areas in these maps represent neighboring areas in the sensory input space.
For example, neighboring areas in the sensory cortex are responsible for the arm and hand regions.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

20. Self-Organizing Maps (Kohonen Maps)
Such topology-conserving mapping can be achieved by SOMs:
Two layers: input layer and output (map) layer
Input and output layers are completely connected.
Output neurons are interconnected within a defined neighborhood.
A topology (neighborhood relation) is defined on the output layer.
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

21. Self-Organizing Maps (Kohonen Maps)
Network structure:
output vector o … O1 O2 O3 Om … x1 x2 xn
input vector x
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

22. Self-Organizing Maps (Kohonen Maps)
Common output-layer structures:
One-dimensional (completely interconnected) i
Two-dimensional (connections omitted, only neighborhood relations shown [green]) i
Neighborhood of neuron i
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

23. Self-Organizing Maps (Kohonen Maps)
A neighborhood function (i, k) indicates how closely neurons i and k in the output layer are connected to each other.
Usually, a Gaussian function on the distance between the two neurons in the layer is used: 
position of i
position of k
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps

24. Unsupervised Learning in SOMs
For n-dimensional input space and m output neurons:
(1) Choose random weight vector wi for neuron i, i = 1, ..., m
(2) Choose random input x
(3) Determine winner neuron k:
||wk – x|| = mini ||wi – x|| (Euclidean distance)
(4) Update all weight vectors of all neurons i in the neighborhood of neuron k: wi := wi + ·(i, k)·(x – wi)
(wi is shifted towards x)
(5) If convergence criterion met, STOP Otherwise, narrow neighborhood function  and learning parameter  and go to (2).
November 16, 2010
Neural Networks Lecture 17: Self-Organizing Maps