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  1. CPN Distance/Similarity Functions • In the hidden layer, the neuron whose weight vector is most similar to the current input vector is the “winner.” • There are different ways of defining such maximal similarity, for example: • (1) Maximal cosine similarity (same as net input): (2) Minimal Euclidean distance: (no square root necessary for determining the winner) Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  2. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + + 2 + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  3. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + + 2 + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  4. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  5. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  6. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  7. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  8. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  9. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  10. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  11. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  12. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  13. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  14. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  15. Counterpropagation – Euclidean Distance • … and so on, • possibly with reduction of the learning rate… Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  16. Counterpropagation – Euclidean Distance + • Example of competitive learning with three hidden neurons: + + 2 + + 3 o o o x 1 o x x x Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  17. The Counterpropagation Network • After the first phase of the training, each hidden-layer neuron is associated with a subset of input vectors. • The training process minimized the average angle difference or Euclidean distance between the weight vectors and their associated input vectors. • In the second phase of the training, we adjust the weights in the network’s output layer in such a way that, for any winning hidden-layer unit, the network’s output is as close as possible to the desired output for the winning unit’s associated input vectors. • The idea is that when we later use the network to compute functions, the output of the winning hidden-layer unit is 1, and the output of all other hidden-layer units is 0. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  18. Counterpropagation – Euclidean Distance • At the end of the output-layer learning process, the outputs of the network are at the center of gravity of the desired outputs of the winner neuron. 2 o x o 3 o x o x x 1 + + + + + Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  19. Now let us talk about… • Neural Network Application Design Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  20. NN Application Design • Now that we got some insight into the theory of artificial neural networks, how can we design networks for particular applications? • Designing NNs is basically an engineering task. • As we discussed before, for example, there is no formula that would allow you to determine the optimal number of hidden units in a BPN for a given task. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  21. NN Application Design • We need to address the following issues for a successful application design: • Choosing an appropriate data representation • Performing an exemplar analysis • Training the network and evaluating its performance • We are now going to look into each of these topics. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  22. Data Representation • Most networks process information in the form of input pattern vectors. • These networks produce output pattern vectors that are interpreted by the embedding application. • All networks process one of two types of signal components: analog (continuously variable) signals or discrete (quantized) signals. • In both cases, signals have a finite amplitude; their amplitude has a minimum and a maximum value. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  23. max min max min Data Representation • analog discrete Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  24. Data Representation • The main question is: • How can we appropriately capture these signals and represent them as pattern vectors that we can feed into the network? • We should aim for a data representation scheme that maximizes the ability of the network to detect (and respond to) relevant features in the input pattern. • Relevant features are those that enable the network to generate the desired output pattern. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  25. Data Representation • Similarly, we also need to define a set of desired outputs that the network can actually produce. • Often, a “natural” representation of the output data turns out to be impossible for the network to produce. • We are going to consider internalrepresentation and externalinterpretation issues as well as specific methods for creating appropriate representations. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  26. Internal Representation Issues • As we said before, in all network types, the amplitude of input signals and internal signals is limited: • analog networks: values usually between 0 and 1 • binary networks: only values 0 and 1allowed • bipolar networks: only values –1 and 1allowed • Without this limitation, patterns with large amplitudes would dominate the network’s behavior. • A disproportionately large input signal can activate a neuron even if the relevant connection weight is very small. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  27. External Interpretation Issues • From the perspective of the embedding application, we are concerned with the interpretation of input and output signals. • These signals constitute the interface between the embedding application and its NN component. • Often, these signals only become meaningful when we define an external interpretation for them. • This is analogous to biological neural systems: The same signal becomes completely different meaning when it is interpreted by different brain areas (motor cortex, visual cortex etc.). Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  28. External Interpretation Issues • Without any interpretation, we can only use standard methods to define the difference (or similarity) between signals. • For example, for binary patterns x and y, we could… • … treat them as binary numbers and compute their difference as | x – y | • … treat them as vectors and use the cosine of the angle between them as a measure of similarity • … count the numbers of digits that we would have to flip in order to transform x into y (Hamming distance) Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  29. External Interpretation Issues • Example: Two binary patterns x and y: • x = 00010001011111000100011001011001001y = 10000100001000010000100001000011110 • These patterns seem to be very different from each other. However, given their external interpretation… x y …x and y actually represent the same thing. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  30. Creating Data Representations • The patterns that can be represented by an ANN most easily are binary patterns. • Even analog networks “like” to receive and produce binary patterns – we can simply round values < 0.5 to 0 and values  0.5 to 1. • To create a binary input vector, we can simply list all features that are relevant to the current task. • Each component of our binary vector indicates whether one particular feature is present (1) or absent (0). Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  31. Creating Data Representations • With regard to output patterns, most binary-data applications perform classification of their inputs. • The output of such a network indicates to which class of patterns the current input belongs. • Usually, each output neuron is associated with one class of patterns. • For any input, only one output neuron should be active (1) and the others inactive (0), indicating the class of the current input. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  32. Creating Data Representations • In other cases, classes are not mutually exclusive, and more than one output neuron can be active at the same time. • Another variant would be the use of binary input patterns and analog output patterns for “classification”. • In that case, again, each output neuron corresponds to one particular class, and its activation indicates the probability (between 0 and 1) that the current input belongs to that class. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  33. Creating Data Representations • Tertiary (and n-ary) patterns can cause more problems than binary patterns when we want to format them for an ANN. • For example, imagine the tic-tac-toe game. • Each square of the board is in one of three different states: • occupied by an X, • occupied by an O, • empty Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  34. Creating Data Representations • Let us now assume that we want to develop a network that plays tic-tac-toe. • This network is supposed to receive the current game configuration as its input. • Its output is the position where the network wants to place its next symbol (X or O). • Obviously, it is impossible to represent the state of each square by a single binary value. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  35. Creating Data Representations • Possible solution: • Use multiple binary inputs to represent non-binary states. • Treat each feature in the pattern as an individual subpattern. • Represent each subpattern with as many positions (units) in the pattern vector as there are possible states for the feature. • Then concatenate all subpatterns into one long pattern vector. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  36. Creating Data Representations • Example: • X is represented by the subpattern 100 • O is represented by the subpattern 010 • <empty> is represented by the subpattern 001 • The squares of the game board are enumerated as follows: Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  37. Creating Data Representations • Then consider the following board configuration: It would be represented by the following binary string: 100 100 001 010 010 100 001 001 010 Consequently, our network would need a layer of 27 input units. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  38. Creating Data Representations • And what would the output layer look like? • Well, applying the same principle as for the input, we would use nine units to represent the 9-ary output possibilities. • Considering the same enumeration scheme: Our output layer would have nine neurons, one for each position. To place a symbol in a particular square, the corresponding neuron, and no other neuron, would fire (1). Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  39. Creating Data Representations • But… • Would it not lead to a smaller, simpler network if we used a shorter encoding of the non-binary states? • We do not need 3-digit strings such as 100, 010, and 001, to represent X, O, and the empty square, respectively. • We can achieve a unique representation with 2-digits strings such as 10, 01, and 00. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  40. Creating Data Representations • Similarly, instead of nine output units, four would suffice, using the following output patterns to indicate a square: Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  41. Creating Data Representations • The problem with such representations is that the meaning of the output of one neuron depends on the output of other neurons. • This means that each neuron does not represent (detect) a certain feature, but groups of neurons do. • In general, such functions are much more difficult to learn. • Such networks usually need more hidden neurons and longer training, and their ability to generalize is weaker than for the one-neuron-per-feature-value networks. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  42. Creating Data Representations • On the other hand, sets of orthogonal vectors (such as 100, 010, 001) can be processed by the network more easily. • This becomes clear when we consider that a neuron’s input signal is computed as the inner product of the input and weight vectors. • The geometric interpretation of these vectors shows that orthogonal vectors are especially easy to discriminate for a single neuron. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  43. Creating Data Representations • Another way of representing n-ary data in a neural network is using one neuron per feature, but scaling the (analog) value to indicate the degree to which a feature is present. • Good examples: • the brightness of a pixel in an input image • the output of an edge filter • Poor examples: • the letter (1 – 26) of a word • the type (1 – 6) of a chess piece Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  44. Creating Data Representations • This can be explained as follows: • The way NNs work (both biological and artificial ones) is that each neuron represents the presence/absence of a particular feature. • Activations 0 and 1 indicate absence or presence of that feature, respectively, and in analog networks, intermediate values indicate the extent to which a feature is present. • Consequently, a small change in one input value leads to only a small change in the network’s activation pattern. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  45. Creating Data Representations • Therefore, it is appropriate to represent a non-binary feature by a single analog input value only if this value is scaled, i.e., it represents the degree to which a feature is present. • This is the case for the brightness of a pixel or the output of an edge detector. • It is not the case for letters or chess pieces. • For example, assigning values to individual letters (a = 0, b = 0.04, c = 0.08, …, z = 1) implies that a and b are in some way more similar to each other than are a and z. • Obviously, in most contexts, this is not a reasonable assumption. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  46. Creating Data Representations • It is also important to notice that, in artificial (not natural!), completely connected networks the order of features that you specify for your input vectors does not influence the outcome. • For the network performance, it is not necessary to represent, for example, similar features in neighboring input units. • All units are treated equally; neighborhood of two neurons does not imply to the network that these represent similar features. • Of course once you specified a particular order, you cannot change it any more during training or testing. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  47. Creating Data Representations • If you wanted to represent the state of each square on the tic-tac-toe board by one analog value, which would be the better way to do this? • <empty> = 0 • X = 0.5 • O = 1 X = 0 <empty> = 0.5 O = 1 Not a good scale!Goes from “neutral” to“friendly” and then“hostile”. More natural scale!Goes from “friendly” to“neutral” and then“hostile”. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  48. Representing Time • So far we have only considered static data, that is, data that do not change over time. • How can we format temporal data to feed them into an ANN in order to detect spatiotemporal patterns or even predict future states of a system? • The basic idea is to treat time as another input dimension. • Instead of just feeding the current data (time t0) into our network, we expand the input vectors to contain n data vectors measured at t0,t0 - t, t0 - 2t, t0 - 3t, …, t0 – (n – 1)t. Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I

  49. $1,000 ? t0-6t t0-5t t0-4t t0-3t t0-2t t0-t t0 t0+t Representing Time • For example, if we want to predict stock prices based on their past values (although other factors also play a role): $0 t Introduction to Artificial Intelligence Lecture 18: Neural Network Application Design I