1. Machine Learning Neural Networks (2)

2. Multi-layers Network Let the network of 3 layers – Input layer – Hidden layer – Output layer Each layer has different number of neurons The famous example to need the multi-layer network is XOR unction The perceptron learning rule can not be applied to multi-layer network We use BackPropagation Algorithm in learning process

7. 7 Feed-forward + Backpropagation Feed-forward: – input from the features is fed forward in the network from input layer towards the output layer Backpropagation: – Method to asses the blame of errors to weights – error rate flows backwards from the output layer to the input layer (to adjust the weight in order to minimize the output error)

8. Backprop Back-propagation training algorithm illustrated: Backprop adjusts the weights of the NN in order to minimize the network total mean squared error. Network activation Error computation Forward Step Error propagation Backward Step

9. Correlation Learning Hebbian Learning (1949): “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes place in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” Weight modification rule:  w i,j = c  x i  x j Eventually, the connection strength will reflect the correlation between the neurons’ outputs.

10. 10 Backpropagation Learning the goal of the Backpropagation learning algorithm is to modify the network’s weights so that its output vector o p = (o p,1, o p,2, …, o p,K ) is as close as possible to the desired output vector d p = (d p,1, d p,2, …, d p,K ) for K output neurons and input patterns p = 1, …, P. The set of input-output pairs (exemplars) {(x p, d p ) | p = 1, …, P} constitutes the training set.

11. Bp Algorithm The weight change rule is Where  is the learning factor <1 Error is the difference between actual and trained value f’ is is the derivative of sigmoid function = f(1-f)

12. Delta Rule Each observation contributes a variable amount to the output The scale of the contribution depends on the input Output errors can be blamed on the weights A least mean square (LSM) error function can be defined (ideally it should be zero) E = ½ (t – y) 2

13. Example For the network with one neuron in input layer and one neuron in hidden layer the following values are given X=1, w1 =1, b1=-2, w2=1, b2 =1,  =1 and t=1 Where X is the input value W1 is the weight connect input to hidden W2 is the weight connect hidden to output B1 and b2 are bias T is the training value

14. Exercises Design a neural network to recognize the problem of X1=[22], t1=0 X=[1-2], t2=1 X3=[-22], t3=0 X4=[-11], t4=1 Start with initial weights w=[0 0] and bias =0

15. Exercises Perform one iteration of backprpgation to network of two layers. First layer has one neuron with weight 1 and bias –2. The transfer function in first layer is f=n2 The second layer has only one neuron with weight 1 and bias 1. The f in second layer is 1/n. The input to the network is x=1 and t=1

16. Neural Network Construct a neural network to solve the problem X1X2Output 1.0 1 9.46.4 2.52.11 8.07.7 0.52.21 7.98.4 7.0 2.80.81 1.23.01 7.86.1 Initialize the weights 0.75, 0.5, and –0.6

17. Neural Network Construct a neural network to solve the XOR problem X1X2Output 110 000 101 011 Initialize the weights –7.0, -7.0, -5.0 and –4.0

18. -0.5 -2 3 1 1 1 1 0.5 The transfer function is linear function. Example

19. Consider a transfer function as f(n) = n2. Perform one iteration of BackPropagation with a= 0.9 for neural network of two neurons in input layer and one neuron in output layer. The input values are X=[1 -1] and t = 8, the weight values between input and hidden layer are w11 = 1, w12 = - 2, w21 = 0.2, and w22 = 0.1. The weight between input and output layers are w1 = 2 and w2= -2. The bias in input layers are b1 = -1, and b2= 3.

20. 20 Some variations learning rate (  ). If  is too small then learning is very slow. If large, then the system's learning may never converge. Some of the possible solutions to this problem are: – Add a momentum term to allow a large learning rate. – Use a different activation function – Use a different error function – Use an adaptive learning rate – Use a good weight initialization procedure. – Use a different minimization procedure

21. Problems with Local Minima backpropagation – Can find its ways into local minima One partial solution: – Random re-start: learn lots of networks Starting with different random weight settings – Can take best network – Or can set up a “committee” of networks to categorise examples Another partial solution: Momentum

22. Adding Momentum Imagine rolling a ball down a hill Without Momentum With Momentum Gets stuck here

23. Momentum in Backpropagation For each weight – Remember what was added in the previous epoch In the current epoch – Add on a small amount of the previous Δ The amount is determined by – The momentum parameter, denoted α – α is taken to be between 0 and 1

24. 24 Momentum Weight update becomes:  w ij (n+1) =  (  pj o pi ) +   w ij (n) The momentum parameter  is chosen between 0 and 1, typically 0.9. This allows one to use higher learning rates. The momentum term filters out high frequency oscillations on the error surface.

25. Problems with Overfitting Plot training example error versus test example error: Test set error is increasing! – Network is overfitting the data – Learning idiosyncrasies in data, not general principles – Big problem in Machine Learning (ANNs in particular)

26. Avoiding Overfitting Bad idea to use training set accuracy to terminate One alternative: Use a validation set – Hold back some of the training set during training – Like a miniature test set (not used to train weights at all) – If the validation set error stops decreasing, but the training set error continues decreasing Then it’s likely that overfitting has started to occur, so stop Another alternative: use a weight decay factor – Take a small amount off every weight after each epoch – Networks with smaller weights aren’t as highly fine tuned (overfit)

27. Feedback NN Recurrent Neural Networks

28. Recurrent Neural Networks Can have arbitrary topologies Can model systems with internal states (dynamic ones) Delays are associated to a specific weight Training is more difficult x1x2 1 0 1 0 1 0 0 0

29. Recurrent neural networks Feedback as well as feedforward connections Allow preservation of information over time Demonstrated capacity to learn sequential behaviors

30. 30 Recurrent neural networks Architectures – Totally recurrent networks – Partially recurrent networks Dynamics of recurrent networks – Continuous time dynamics – Discrete time dynamics Associative memories Solving optimization problems

31. Input-Output Recurrent Model Input-Output Recurrent Model → nonlinear autoregressive with exogeneous inputs model (NARX) y(n+1) = F(y(n),...,y(n- q+1),u(n),...,u(n-q+1)) The model has a single input. It has a single output that is fed back to the input. The present value of the model input is denoted u(n), and the corresponding value of the model output is denoted by y(n+1).

32. Recurrent multilayer perceptron (RMLP) It has one or more hidden layers. Each computation layer of an RMLP has feedback around it. x I (n+1) =  I (x I (n),u(n)) x II (n+1) =  II (x II (n),x I (n+1)),..., x O (n+1) =  O (x O (n), x K (n))

33. The equivalence between layered, feedforward nets and recurrent nets w 1 w 2 w 3 w 4 w 1 w 2 w 3 w 4 w 1 w 2 w 3 w 4 w 1 w 2 w 3 w 4 time=0 time=2 time=1 time=3 Assume that there is a time delay of 1 in using each connection. The recurrent net is just a layered net that keeps reusing the same weights.

34. Recurrent Neural Networks : Hopfield Network Proper when exact binary representations are possible. Can be used as an associative memory or to solve optimization problems. The number of classes (M) must be kept smaller than 0.15 times the number of nodes (N).

35. x0x0 x1x1 x N-2 x N-1 x’0x’0 x’1x’1 x’ N-2 x’ N-1..... INPUTS(Applied At Time Zero) OUTPUTS(Valid After Convergence) Hopfield NN

36. Recurrent Neural Networks : Hopfield Network Algorithm Step 1 : Assign Connection Weights. Step 2 : Initialize with unknown input pattern.

37. Step 3 : Iterate until convergence. Step 4 : goto step 2. 1 0 Recurrent Neural Networks : Hopfield Network Algorithm

38. Example Illustrate your understanding of the Recurrent back propagation Neural Networks by explicitly showing all steps of the calculations with a Sigmoidal nonlinearity and  =0.8 for neural network blow. The input values are X=[1 1] and t = 8, the initial weight values are w1=1, w2=-1, w3=1, w4=1, w5 =2, and w6= -2. Show all the calculations for ONE iteration. Show the weight values at the end of the first iteration?

39. 39 The illustrated Simple Recurrent Neural Network has two neurons. All neurons have sigmoid function. The network ues the standard error function E = using the initial weights [b1=-0.5, w1=2,b2=0.5 and w2=0.5] and let the input = 2,  = 1 and t = 5. Perform two iterations of recurrent back- propagation algorithm.

41. Unsupervised Learning Supervised learning, in which an external teacher improves network performance by comparing desired and actual outputs and modifying the synaptic weights accordingly. However, most of the learning that takes place in our brains is completely unsupervised. This type of learning is aimed at achieving the most efficient representation of the input space, regardless of any output space.

42. Unsupervised learning The network must discover for itself patterns, features, regularities,correlations or categories in the input data and code them for the output. The units and connections must self-organize themselves based on the stimulus they receive. Note that unsupervised learning is useful when there is redundancy in the input data. Redundancy provides knowledge.

43. Unsupervised Learning Applications of unsupervised learning include Clustering Vector quantization Data compression Feature extraction

44. Self-organising maps (SOMs) Inspiration from Biology: In auditory pathway nerve cells arranged in relation to frequency response (tonotopic organisation). Kohonen took inspiration from to produce self- organising maps (SOMs). In SOM units located physically next to one another will respond to input vectors that are ‘similar’.

45. SOMs Useful, as difficult for Humans to visualise when data has > 3 dimensions. Large dimensional input vectors 'projected down' onto 2-D map in way maintaining natural order similarity. SOM is 2-D array of neurons, all inputs arriving at all neurons.

46. SOMs Initially each neuron has own set of (random) weights. When input arrives neuron with pattern of weights most similar to input gives largest response.

47. SOMs Positive excitatory feedback between SOM unit and nearest neighbours. Causes all the units in ‘neighbourhood’ of winner unit to learn. As distance from winning unit increases degree of excitation falls until it becomes inhibition. Bubble of activity (neighbourhood) around unit with largest net input (Mexican-Hat function).

48. SOMs Initially each weight set to random number. Euclidean distance D used to find difference between input vectors and weights of SOM units (D = square root of the sum of the squared differences) =

49. SOMs For a 2-dimensional problem, the distance calculated in each neuron is:

50. SOM Input vector simultaneously compared to all elements in network, one with lowest D is winner. Update weights all in neighbourhood around winning unit. If winner is ‘c’, neighbourhood defined as being Mexican Hat function around ‘c’.

51. SOMs Weights of units are adjusted using:  w ij = k(x i – w ij )Y j Where Y j from Mexican Hat function k is a value which changes over time (high at start of training, low later on).

52. Two distinct phases in training Initial ordering phase: units find correct topological order (might take 1000 iterations where k decreases from 0.9 to 0.01, Nc decreases l from ½ diameter of the network to 1 unit. Final convergence phase: accuracy of weights improves. (k may decrease from 0.01 to 0 while Nc stays at 1 unit. Phase could be 10 to 100 times longer depending on desired accuracy.

53. 53 WEBSOM All words of document are mapped into the word category map Histogram of “hits” on it is formed Self-organizing map. Largest experiments have used: word-category map 315 neurons with 270 inputs each Document-map 104040 neurons with 315 inputs each Self-organizing semantic map. 15x21 neurons Interrelated words that have similar contexts appear close to each other on the map Self-organizing maps of document collections.

54. NN 454 WEBSOM

55. Clustering Data

56. K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

57. K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

58. K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

59. K-Means Animation Example generated by Andrew Moore using Dan Pelleg’s super- duper fast K-means system: Dan Pelleg and Andrew Moore. Accelerating Exact k-means Algorithms with Geometric Reasoning. Proc. Conference on Knowledge Discovery in Databases 1999.

60. 60 K-means Clustering – Initialize the K weight vectors, e.g. to randomly chosen examples. Each weight vector represents a cluster. – Assign each input example x to the cluster c(x) with the nearest corresponding weight vector: – Update the weights: – Increment n by 1 and go until no noticeable changes of weight vectors occur.

61. Simple competitive learning

62. 62 Issues How many clusters? – User given parameter K – Use model selection criteria (Bayesian Information Criterion) with penalization term which considers model complexity. See e.g. X- means: http://www2.cs.cmu.edu/~dpelleg/kmeans.html What similarity measure? – Euclidean distance – Correlation coefficient – Ad-hoc similarity measure How to assess the quality of a clustering? – Compact and well separated clusters are better … many different quality measures have been introduced. See e.g. C. H. Chou, M. C. Su and E. Lai, “A New Cluster Validity Measure and Its Application to Image Compression,” Pattern Analysis and Applications, vol. 7, no. 2, pp. 205-220, 2004. (SCI)