1. Neural Networks - Berrin Yanıkoğlu1 Applications and Examples From Mitchell Chp. 4

2. 2 ALVINN drives70mph on highways

3. 3 Speech Recognition

4. 4 Hidden Node Functions

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10. 10 Head Pose Recognition

11. Neural Networks - Berrin Yanıkoğlu11 MLP & Backpropagation Issues

12. 12 Considerations Network architecture Typically feedforward, however you may also use local receptive fields for hidden nodes; and in sequence learning, recursive nodes Number of input, hidden, output nodes Number of hidden nodes is quite important, others determined by problem setup Activation functions Careful: regression requires linear activation on the output For others, sigmoid or hyperbolic tangent is a good choice Learning rate Typically software adjusts this

13. 13 Considerations Preprocessing Important (see next slides) Learning algorithm Backpropagation with momentum or Levenberg-Marquart suggested When to stop training Important (see next slides)

14. 14 Preprocessing Input variables should be decorrelated and with roughly equal variance But typically, a very simple linear transformation is applied to the input to obtain zero-mean - unit variance input: x i = ( x i - x i _ mean )/  i where  i = 1/(N-1)  ( x pi - x i _ mean ) 2 patterns p More complex preprocessing is also commonly done: E.g. Principal component analysis

15. 15 When to stop training No precise formula: 1a) At local minima, the gradient magnitude is 0 –Stop when the gradient is sufficiently small need to calculate the gradient over the whole set of patterns May need to measure the gradient in several directions, to avoid errors caused by numerical instability 1b) Local minima is a stationary point of the performance index (the error) –Stop when the absolute change in weights is small How to measure? Typically, rates: 0.01% 2) We are interested in generalization ability –Stop when the generalization, measured as the performance on validation set, starts to increase

16. 16 Effects of Sequential versus Batch Mode: Summary –Batch: –Better estimation of the gradient –Sequential (online) –Better if data is highly correlated –Better in terms of local minima (stochastic search) –Easier to implement

17. Neural Networks - Berrin Yanıkoğlu17 Performance Surface Motivation for some of the practical issues

18. 18 Local Minima of the Performance Criteria - The performance surface is a very high-dimensional (W) space full of local minima. - Your best bet using gradient descent is to locate one of the local minima. –Start the training from different random locations (we will later see how we can make use of several thus trained networks) –You may also use simulated annealing or genetic algorithms to improve the search in the weight space.

19. 19 Performance Surface Example Network ArchitectureNominal Function Parameter Values Layer numbers are shown as superscripts

20. 20 Squared Error vs. w 1 1,1 and b 1 1 w 1 1,1 b11b11 b11b11

21. 21 Squared Error vs. w 1 1,1 and w 2 1,1 w 1 1,1 w 2 1,1 w 1 1,1 w 2 1,1

22. 22 Squared Error vs. b 1 1 and b 1 2 b11b11 b21b21 b21b21 b11b11

23. Neural Networks - Berrin Yanıkoğlu23 MLP & Backpropagation Summary REST of the SLIDES are ADVANCED MATERIAL (read only if you are interested, or if there is something you do^’t understand…) These slides are thanks to John Bullinaria

24. Gradient Descent Learning Summary: –The purpose of neural network learning or training is to minimise the output errors on a particular set of training data by adjusting the network weights wij. –We start by defining an appropriate Error or Cost Function E(wij) that “measures” how far the current network is from the desired (correctly trained) one. –Gradients given by partial derivatives of the error function ∂E(wij)/∂wij then tell us which direction we need to move in weight space to reduce the error. –The gradients are multiplied by a learning rate η that specifies the step sizes we take in weight space for each iteration of the weight update equation. –We keep stepping through weight space until the errors are “small enough”. 24

25. If the neuron activation functions have derivatives that take on particularly simple forms, that can make the weight update computations very efficient. These factors lead to powerful learning algorithms for training neural networks. 25

26. Practical Considerations for Gradient Descent Learning The general idea is straightforward, but there remain a number of important questions about training single layer neural networks that still need to be resolved: –Do we need to pre-process the training data? If so, how? –How do we choose the initial weights from which the training is started? –How do we choose an appropriate learning rate η? –Should we change the weights after each training pattern, or after the whole set? –How can we avoid local minima in the error function? –How can we avoid flat spots in the error function? –How do we know when we should stop the training? We shall now consider each of these practical issues in turn. 26

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41. Neural Networks - Berrin Yanıkoğlu41 Alternatives to Gradient Descent ADVANCED MATERIAL (read only if interested)

42. 42 SUMMARY There are alternatives to standard backpropagation, intended to deal with speeding up its convergence. These either choose a different search direction (p) or a different step size (  ). In this course, we will cover updates to standard backpropagation as an overview, namely momentum and variable rate learning, skipping the other alternatives (those that do not follow steepest descent, such as conjugate gradient method). –Remember that you are never responsible of the HİDDEN slides (that do not show in show mode but are visible when you step through the slides!)

43. 43 Variations of Backpropagation –Momentum: Adds a momentum term to effectively increase the step size when successive updates are in the same direction. –Adaptive Learning Rate: Tries to increase the step size and if the effect is bad (causes oscillations as evidenced by a decrease in performance) Newton’s Method: Conjugate Gradient Levenberg-Marquardt Line search

44. 44 Motivation for momentum (Bishop 7.5)

45. 45 Effect of momentum  w ij (n) =  E/dw ij (n) +   w ij (n-1) n  w ij (n) =    n-t  E/dw ij (t) t=0 If same sign in consecutive iterations => magnitude grows If opposite sign in consecutive iterations => magnitude shrinks For  w ij (n) not to diverge,  must be < 1. Effectively adds inertia to the motion through the weight space and smoothes out the oscillations The smaller the , the smoother the trajectory

46. 46 Effect of momentum

47. 47 Effect of momentum (Bishop 7.7)

48. 48 Convergence Example of Backpropagation w 1 1,1 w 2 1,1

49. 49 Learning Rate Too Large w 1 1,1 w 2 1,1

50. 50 Momentum Backpropagation w 1 1,1 w 2 1,1

51. 51 Variable Learning Rate If the squared error decreases after a weight update the weight update is accepted the learning rate is multiplied by some factor  >1. If the momentum coefficient  has been previously set to zero, it is reset to its original value. If the squared error (over the entire training set) increases by more than some set percentage  after a weight update weight update is discarded the learning rate is multiplied by some factor (1  >    >  0) the momentum coefficient  is set to zero. If the squared error increases by less than , then the weight update is accepted, but the learning rate and the momentum coefficient are unchanged.

52. 52 Example w 1 1,1 w 2 1,1