Νεςπο-Ασαυήρ Υπολογιστική Neuro-Fuzzy Computing

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1 Νεςπο-Ασαυήρ Υπολογιστική Neuro-Fuzzy Computing ΗΥ418 Διδάσκων Δημήτριος Τμ. ΗΜΜΥ Πανεπιστήμιο Θεσσαλίαρ Διάλεξη 10η 1

2 Numerical optimization techniques applied to Backpropagation: Conjugate Gradient (CGBP) 2

3 Conjugate Gradient Recall from your Numerical Analysis course: Steepest Descent is the simplest algorithm, but is often slow in converging Newton s method is much faster, but requires that the Hessian matrix and its inverse be calculated Conjugate Gradient it does not require the calculation of second derivatives, and yet it still has the quadratic convergence property. (It converges to the minimum of a quadratic function in a finite number of iterations We will describe how the conjugate gradient algorithm can be used to train multilayer networks We will call this algorithm Conjugate Gradient BackPropagation (CGBP)

4 Background on Conjugate Gradient 4

5 Προσέγγιση κατά Taylor μιας συνάρτησης Ανάπτυγμα της σειράς Taylor μέχρι πρώτης και δεύτερης τάξης για την πολυδιάσταση συνάρτηση f(x) γύρω από το σημείο x* (ελάχιστο) και αποτελούν γραμμική και τετραγωνική προσέγγιση της συνάρτησης, αντίστοιχα

6 Προσέγγιση κατά Taylor μιας συνάρτησης Ισοδύναμα έχουμε: όπου το ξ k βρίσκεται πάνω στο ευθύγραμμο τμήμα που ορίζεται από τα σημεία x k και x* Αυτή η συνάρτηση ονομάζεται τετραγωνική μορφή (quadratic form την ξανασυναντήσαμε στην εκπαίδευση της ADALINE) και έχει την γενική μορφή:

7 Τπενθύμιση της Steepest Descent ε κάθε βήμα, η Steepest Descent προσεγγίζει την αντικειμενική συνάρτηση κάνοντας χρήση της πρώτης παραγώγου (της πρώτης εξίσωσης της προηγούμενης διαφάνειας) όπου το λ k βρίσκεται με βελτιστοποίηση Εάν όμως η συνάρητηση είναι τετραγωνική μορφή, τότε:

8 υζυγείς διευθύνσεις ΟΡΙΜΟ. Μια μέθοδος έχει την ιδιότητα του τετραγωνικού τερματισμού όταν συγκλίνει στο βέλτιστο σημείο x* μιας τετραγωνικής αντικειμενικής συνάρτησης σε γνωστό πεπερασμένο αριθμό επαναλήψεων ΟΡΙΜΟ. Ένα σύνολο n γραμμικώς ανεξαρτήτων μη μηδενικών διανυσμάτων s 1, s 2,, s n είναι συζυγή ως προς έναν θετικά ορισμένο πίνακα H εάν s i Hs j =0 1 i j n ΟΡΙΜΟ. Μια μέθοδος βελτιστοποίησης ονομάζεται μέθοδος συζυγών διευθύνσεων εάν κατά την εφαρμογή της παράγει συζυγείς διευθύνσεις και εφαρμοζόμενη σε μια τετραγωνική μορφή με Hessian πίνακα H έχει την ιδιότητα του τετραγωνικού τερματισμού

9 υζυγείς διευθύνσεις ΘΕΩΡΗΜΑ. ε κάθε αντικειμενική συνάρτηση που είναι τετραγωνική μορφή και έχει ένα ελάχιστο, εάν ακολουθήσουμε μια μέθοδο που παράγει συζυγείς ως προς τον Hessian πίνακα διευθύνσεις, το ελάχιστο θα εντοπιστεί σε n το πολύ βήματα, ένα για κάθε συζυγή διεύθυνση ΘΕΩΡΗΜΑ. Εάν f(x) είναι τετραγωνική μορφή, και s 0, s 1,, s k σύνολο συζυγών ως προς τον Hessian πίνακα διευθύνσεων, οι οποίες παράγονται με κάποια μέθοδο, μέχρι την προσέγγιση x k+1, τότε: T f(x k+1 )s j = 0 για j=1,2, k

10 Παράδειγμα ΠΡΟΒΛΗΜΑ. Να εντοπιστεί το ελάχιστο της f(x)=x 2 1+5x ΛΤΗ. Η κλίση είναι f(x)= (2x 1, 10x 2 ) Σ Ο Hessian είναι: Ας εκκινήσουμε από το x 0 =(2 2) T Σότε, f(x 0 )= (4, 20) Σ Επιλέγοντας τυχαία μια διεύθυνση εκκίνησης, έστω την s 0 =(1/2, 3/2)T που έχει s 0 =1, παίρνω ότι λ 0 = Δεν είναι θετικό!!! γιατί η s 0 δεν είναι διεύθυνση μείωσης x 1 =x 0 +λ 0 s 0 =...=

11 Παράδειγμα Η κλίση εδώ είναι f(x 1 )= ( , ) Σ Θέλουμε τώρα να υπολογίσουμε την επόμενη διεύθυνση ελαχιστοποίησης με τέτοιον τρόπο ώστε να είναι συζυγής ως προς την s 0 σε σχέση με τον Hessian H και να είναι μοναδιαία, δηλαδή να ισχύουν: s T 1Hs 0 =0 και s T 1s 1 =1 Εάν συμβολίσουμε s 1 = (s 1 1,s 1 2) προκύπτει από τις προηγούμενες εξισώσεις ότι: Από τις δυο λύσεις του συστήματος αυτού, επιλέγουμε εκείνη που δίνει διεύθυνση μείωσης, δηλαδή να ισχύει: s T 1 f(x 1 ) < 0

12 Παράδειγμα Αυτή είναι η s 1 = ( , ) Σ Αυτή είναι η επόμενη διεύθυνση, και είναι μοναδιαία και συζυγή της προηγούμενης, κατά μήκος της οποίας θα κινηθούμε με βήμα λ 1 Από τον τύπο της Διαφάνειας-5, έχω ότι λ 1 = Παρτηρήστε ότι το λ 1 είναι θετικό όπως θα έπρεπε αφού κινούμαστε σε διεύθυνση μείωσης Η επόμενη προσέγγιση είναι: x 2 = x 1 +λ 1 s 1 = (0 0) T Αυτό είναι το βέλτιστο και βρέθηκε σε 2 βήματα (είναι τεταγωνική η αντικειμενική συνάρτηση με διάσταση 2)

13 Μέθοδοι συζυγών κλίσεων Προτάθηκε αρχικά από τους Hestenes & Stiefel (1952) και Beckman (1960) Γνωστότερη παραλλαγή αυτή των Fletcher-Reeves (1964) Παράγει στο βήμα k την διεύθυνση s k η οποία είναι γραμμικός συνδυασμός της - f(x k ) δηλαδή της διεύθυνσης της μέγιστης αλλαγής στην τρέχουσα προσέγγιση, και των προηγούμενων διευθύνσεων s 0, s 1,, s k-1 Οι συντελεστές του γραμμικού συνδυασμού επιλέγονται ώστε οι παραγόμενες διευθύνσεις από προσέγγιση σε προσέγγιση να είναι συζυγείς ως προς τον Hessian πίνακα της αντικειμενικής συνάρτησης ΣΕΛΙΚΑ προκύπτει ότι για να υπολογίσουμε αυτούς τους συντελεστές χρειαζόμαστε μόνο την τρέχουσα κλίση f(x k ) και την ακριβώς προηγούμενη f(x k-1 )

14 Fletcher-Reeves conjugate gradient

15 Fletcher-Reeves conjugate gradient

16 Παραλλαγές μεθόδων συζυγών κλίσεων Ακολουθούν τον προηγούμενο αλγόριθμο, αλλά διαφέρουν στον τύπο υπολογισμού του ω k Μέθοδος του Daniel (1967) Μέθοδος των Crowder & Wolfe (1971) Μέθοδος των Polak & Ribiere (1969), Polyak (1969)

17 Drawbacks of basic Conjugate Gradient This conjugate gradient algorithm cannot be applied directly to the neural network training task, because the performance index is not quadratic. This affects the algorithm in two ways: First, we can not minimize the function along a line, as required in Step 2 Second, the exact minimum will not normally be reached in a finite number of steps, and therefore the algorithm will need to be reset after some set number of iterations

18 Interval location Let s address the linear search first We need to have a general procedure for locating the minimum of a function in a specified direction This will involve two steps: interval location interval reduction The purpose of the interval location step is to find some initial interval that contains a local minimum The interval reduction step then reduces the size of the initial interval until the minimum is located to the desired accuracy

19 Interval location We will use a function comparison method to perform the interval location step, illustrated in the following figure We begin by evaluating the performance index at an initial point, represented by α 1. This point corresponds to the current values of the network weights and biases. In other words, we are evaluating F(x 0 ) The next step is to evaluate the function at a second point, represented by b 1, which is a distance ε from the initial point, along the first search direction p 0. In other words, we are evaluating F(x 0 +εp 0 ) This process stops when the function increases between two consecutive evaluations. The minimum is bracketed by the two points α 5 and b 5. The minimum may occur either in the interval [α 3,b 3 ] or in the interval [α 4,b 4 ].

20 Interval reduction The next step is interval reduction, which involves evaluating the function at points inside the interval [a 5,b 5 ] From the figure below we can see that we will need to evaluate the function at two internal points (at least) in order to reduce the size of the interval of uncertainty The let figure shows that one internal function evaluation does not provide us with any information on the location of the minimum However, if we evaluate the function at two points c and d, as in the right figure, we can reduce the interval of uncertainty If F(c) > F(d), then the minimum must occur in the interval [c, b] If F(c) < F(d), then the minimum must occur in the interval [a, d] We are assuming that there is a single minimum located in the initial interval.

21 Interval reduction: Golden section search We need to determine the locations of points c and d We will use Golden Section search, which is designed to reduce the number of function evaluations required At each iteration one new function evaluation is required τ is user-set tolerance

22 Final adjustments to develop the CGBP There is one more modification to the conjugate gradient algorithm that needs to be made before we apply it to neural network training For quadratic functions the algorithm will converge to the minimum in at most n iterations, where n is the number of parameters being optimized The mean squared error performance index for multilayer networks is not quadratic, therefore the algorithm would not normally converge in n iterations The development of the conjugate gradient algorithm does not indicate what search direction to use once a cycle of n iterations has been completed There have been many procedures suggested, but the simplest method is to reset the search direction to the steepest descent direction (negative of the gradient) after n iterations

23 Critique of CGBP CGBP algorithm converges in many fewer iterations than other algorithms This is a little deceiving, since each iteration of CGBP requires more computations than these other methods there are many function evaluations involved in each iteration of CGBP Even so, CGBP has been shown to be one of the fastest batch training algorithms for multilayer networks C. Charalambous, Conjugate gradient algorithm for efficient training of artificial neural networks, IEE Proceedings, vol. 139, no. 3, pp , 1992

24 Numerical optimization techniques applied to Backpropagation: Levenberg-Marquardt algorithm 24

25 Levenberg-Marquardt basic algorithm The Levenberg-Marquardt algorithm is a variation of Newton s method that was designed for minimizing functions that are sums of squares of other nonlinear functions This is very well suited to neural network training (recall that their performance index is the mean squared error) Newton s method for optimizing a performance index F(x) is as follows: where and

26 Levenberg-Marquardt basic algorithm If we assume that F(x) is a sum of squares function then the j-th element of the gradient would be The gradient can therefore be written in matrix form where the Jacobian matrix is:

27 Levenberg-Marquardt basic algorithm Next we wish to find Hessian matrix. The k,j element would be: The Hessian can be expressed in matrix form: where If we assume that S(x) is small, the we can approximate

28 Levenberg-Marquardt basic algorithm So, the Newton method evolves into the Gauss-Newton method: One problem with the Gauss-Newton method is that the matrix H=J T J may not be invertible This can be overcome by using the following modification to the approximate Hessian matrix: G=H+μI (Figure out why this matrix can be made invertible)

29 Levenberg-Marquardt basic algorithm This lead to the Levenberg-Marquardt algorithm: Or The algorithm begins with μ k set to some small value (e.g., 0.01) If a step does not yield a smaller value for F(x), then the step is repeated with μ k multiplied by some factor θ>1 (e.g., θ=10) Eventually F(x) should decrease, since we would be taking a small step in the direction of steepest descent If a step does produce a smaller value for F(x), then μ k is divided by θ for the next step, so that the algorithm will approach Gauss-Newton, which should provide faster convergence The algorithm provides a nice compromise between the speed of Newton s method and the guaranteed convergence of steepest descent

30 Training with Levenberg-Marquardt The performance index for multilayer network training is the mean squared error If each target occurs with equal probability, the mean squared error is proportional to the sum of squared errors over the Q targets in the training set: This is equivalent to the performance index for which Levenberg-Marquardt was designed It should be a straightforward matter to adapt the algorithm for network training, but it turns out that it does require some care in working out the details

31 Training with Levenberg-Marquardt The key step in the Levenberg-Marquardt algorithm is the computation of the Jacobian matrix To perform this computation we will use a variation of the backpropagation algorithm To create the Jacobian matrix we need to compute the derivatives of the errors, instead of the derivatives of the squared errors (as we did for the standard backpropagation)

32 Jacobian calculation Recall Jacobian s form from (slide 14) Error vector: Parameter vector: N=Q x S M and n=s 1 (R+1)+S 2 (S 1 + 1)+ +S M (S M-1 + 1) So:

33 Jacobian calculation The terms in this Jacobian matrix can be computed by a simple modification to the backpropagation algorithm Standard backpropagation calculates terms like: For the elements of the Jacobian matrix that are needed for the Levenberg-Marquardt algorithm we need to calculate terms like: In our derivation of standard backpropagation: where

34 Marquardt sensitivity The backpropagation process computed the sensitivities through a recurrence relationship from the last layer backward to the first layer We can use the same concept to compute the terms needed for the Jacobian matrix if we define Marquardt sensitivity: where h=(q-1)s M + k We can compute the elements of the Jacobian or if x l is a bias

35 Marquardt sensitivity Marquardt sensitivities can be computed through the same recurrence relations as the standard sensitivities with one modification at the final layer For the Marquardt sensitivities at the final layer we have: Therefore when the input p q has been applied to the network and the corresponding network a M q output has been computed, the Levenberg-Marquardt backpropagation is initialized with: where F M (n M )is define as in standard backpropagation The columns can also be backpropagated together using

36 Marquardt sensitivity The total Marquardt sensitivity matrices for each layer are then created by augmenting the matrices computed for each input: Note that for each input that is presented to the network we will backpropagate S M sensitivity vectors This is because we are computing the derivatives of each individual error, rather than the derivative of the sum of squares of the errors. For every input applied to the network there will be S M errors (one for each element of the network output) For each error there will be one row of the Jacobian matrix After the sensitivities have been backpropagated, the Jacobian matrix is computed using equation of slide 22

37 Levenberg-Marquardt backpropagation 1. Present all inputs to the network and compute the corresponding outputs and the errors e q =t q -a M q. Compute the sum of squared errors over all inputs, F(x) (slide 18) 2. Compute the Jacobian matrix. Calculate the sensitivities with the recurrence relations (equations in slide 23). Augment the individual matrices into the Marquardt sensitivities. Compute the elements of the Jacobian matrix with equations in slide Solve equation in slide 17 to obtain Δx k 4. Recompute the sum of squared errors using x k +Δx k. If this new sum of squares is smaller than that computed in Step-1, then divide μ by θ, let x k+1 =x k +Δx k and go back to Step-1. If the sum of squares is not reduced, then multiply μ by θ and go back to Step-3

38 Critique of LMBP Even given the large number of computations, however, the LMBP algorithm appears to be the fastest neural network training algorithm for moderate numbers of network parameters M. T. Hagan and M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, vol. 5, no. 6, The key drawback of the LMBP algorithm is the storage requirement: The algorithm must store the approximate Hessian matrix J T J This is an nxn matrix, where n is the number of parameters (weights and biases) Recall that the other methods discussed need only store the gradient, which is an n-dimensional vector When the number of parameters is very large, it may be impractical to use the Levenberg-Marquardt algorithm

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