Intelligent Systems Discriminative Learning, Neural Networks

Transcription

1 Intelligent Systems Discriminative Learning, Neural Networks Carsten Rother, Dmitrij Schlesinger WS2014/2015,

2 Outline 1. Discriminative learning 2. Neurons and linear classifiers: 1) Perceptron-Algorithm 2) Non-linear decision rules 3. Feed-Forward Neural Networks: 1) Architecture 2) Modeling abilities 3) Learning Error Back-Propagation Intelligent Systems: Discriminative Learning, Neural Networks 2

3 Discriminant Functions Let a parameterized family of probability distributions be given. Each particular p.d. leads to a classifier (for a fixed loss). The final goal is the classification (applying the classifier). Generative approach: 1. Learn the parameters of the probability distribution (e.g. ML) 2. Derive the corresponding classifier (e.g. Bayes) 3. Apply the classifier for test data Discriminative approach: 1. Learn the unknown parameters of the classifier directly 2. Apply the classifier for test data If the family of classifiers is well parameterized, it is not necessary to consider the underlying probability distribution at all!!! Intelligent Systems: Discriminative Learning, Neural Networks 3

4 Example: two Gaussians Assume, we know the probability model: two Gaussians of equal variance, i.e. k 2 {1, 2}, x 2 R n, p(k, x) =p(k) p(x k) p(x k) = 1 ( p 2 ) n exp apple x µk Assume, at the end we want to do the Maximum A-posteriori Decision Consequently (see the previous lecture), the classifier is a separating plane (a linear classifier). Let us search during the learning just for a good separating plane instead to learn the unknown parameters of the underlying probability model Intelligent Systems: Discriminative Learning, Neural Networks 4

5 Neuron Hunan vs. Computer (two nice pictures from Wikipedia) Intelligent Systems: Discriminative Learning, Neural Networks 5

6 Neuron (McCulloch and Pitt, 1943) Input: Weights: Activation: Output: x 2 R n w 2 R n b 2 R y = f(y 0 b)=f(hw, xi b) Step-function f(y 0 )= 1 if y 0 > 0 0 otherwise hx, wi 7 b Sigmoid-function (differentiable!!!) f(y 0 )= exp( y 0 ) Intelligent Systems: Discriminative Learning, Neural Networks 6

7 Geometric interpretation hx, wi = x w cos Let w be normalized, i.e. w =1 ) x cos is the length of the projection of x onto w. Separating plane: hx, wi = const Neuron implements a linear classifier Intelligent Systems: Discriminative Learning, Neural Networks 7

8 A special case boolean functions Input: Output: x =(x 1,x 2 ),x i 2 {0, 1} y = x 1 &x 2 Find w and b so, that step(w 1 x 1 + w 2 x 2 b)=x 1 &x 2 x 1 x 2 y w 1 = w 2 =1, b =1.5 Disjunction, other boolean functions, but XOR Intelligent Systems: Discriminative Learning, Neural Networks 8

9 The (one possible) learning task Given: training data L = (x 1,y 1 ), (x 2,y 2 )...(x L,y L ),x l 2 R n,y l 2 {0, 1} Find: w 2 R n,b2 R so that f(hx l,wi b) =y l for all l =1,...,L For a step-neuron: system of linear inequalities hx l,wi >b if y l =1 hx l,wi <b if y l =0 Solution is not unique in general!!! Intelligent Systems: Discriminative Learning, Neural Networks 9

10 Preparation 1 Eliminate the bias: The trick modify the training data x =(x 1,x 2,...,x n ) ) x =(x 1,x 2,...,x n, 1) w =(w 1,w 2,...,w n ) ) w =(w 1,w 2,...,w n,b) hx l,wi 7 b ) h x l, wi 7 0 Example in 1D non-separable without the bias separable without the bias Intelligent Systems: Discriminative Learning, Neural Networks 10

11 Preparation 2 Remove the sign: The trick the same ˆx l = x l for all l with y l =1 ˆx l = x l for all l with y l =0 All in all: hx l,wi >b if y l =1 hx l,wi <b if y l =0 ) hˆx l, wi > 0 Intelligent Systems: Discriminative Learning, Neural Networks 11

12 Perceptron Algorithm (Rosenblatt, 1958) Solution of a system of linear inequalities: 1. Search for an equation that is not satisfied, i.e. hx l,wiapple0 2. If not found Stop else update w new = w old + x l go to 1. The algorithm terminates in a finite number of steps if a solution exists (the training data are separable) The solution is a convex combination of the data points Intelligent Systems: Discriminative Learning, Neural Networks 12

13 An example problem Consider another decision rule for a real valued feature x 2 R : a n x n + a n 1 x n a 1 x + a 0 = X i a i x i 7 0 It is not a linear classifier anymore but a polynomial one. The task is again to learn the unknown coefficients a i given the training data (x l,y l )..., x l 2 R, y l 2 {0, 1} Is it also possible to do that in a Perceptron-like fashion? Intelligent Systems: Discriminative Learning, Neural Networks 13

14 An example problem The idea: reduce the given problem to the Perceptron-task. Observation: although the decision rule is not linear with respect to x, it is still linear with respect to the unknown coefficients a i The same trick again modify the data: w =(a n,a n 1,...,a 1,a 0 ) x =(x n,x n 1,...,x,1) ) X i a i x i = h x, wi In general, it is very often possible to learn non-linear decision rules by the Perceptron algorithm using an appropriate transformation of the input space (more examples at seminars). Extension Support Vector Machines, Kernels Intelligent Systems: Discriminative Learning, Neural Networks 14

15 Many classes Before: two classes a mapping Now: many classes a mapping R n! {0, 1} R n! {1, 2,...,K} How to generalize? How to learn? Two simple (straightforward) approaches: The first one: one vs. all there is one binary classifier per class, that separates this class from all others. The classification is ambiguous in some areas. Intelligent Systems: Discriminative Learning, Neural Networks 15

16 Many classes Another one: pairwise classifiers there is a classifier for each class pair Less ambiguous, better separable. However: K(K 1)/2 binary classifiers instead of K in the previous case. The goal: no ambiguities, K parameter vectors ) Fisher Classifier Intelligent Systems: Discriminative Learning, Neural Networks 16

17 Fisher classifier Idea: in the binary case the output greater is the scalar product hx, wi y is the more likely to be 1 the generalization: y = arg max hx, w ki k Geometric interpretation (let be normalized) w k Consider projections of an input vector onto vectors w k x The input space is partitioned into the set of convex cones. Intelligent Systems: Discriminative Learning, Neural Networks 17

18 Feed-Forward Neural Networks Output level i-th level First level Input level X y ij = f w ijj 0y i 1j 0 b ij j 0 Special case: m =1, Step-neurons a mapping R n! {0, 1} Intelligent Systems: Discriminative Learning, Neural Networks 18

19 What we can do with it? One level single step-neuron linear classifier Intelligent Systems: Discriminative Learning, Neural Networks 19

20 What we can do with it? Two levels, & -neuron as the output intersection of half-spaces If the number of neurons is not limited, all convex subspaces can be implemented with an arbitrary precision. Intelligent Systems: Discriminative Learning, Neural Networks 20

21 What we can do with it? Three levels all possible mappings subspaces: R n! {0, 1} as union of convex Three levels (really even less) are enough to implement all possible mappings!!! Intelligent Systems: Discriminative Learning, Neural Networks 21

22 Learning Error Back-Propagation Learning task: Given: training data (x l,k l ),..., x l 2 R n, k l 2 R Find: all weights and biases of the net. Error Back-Propagation is a gradient descent method for Feed- Forward-Networks with Sigmoid-neurons First, we need an objective (error to be minimized) F (w, b) = X l k l y(x l ; w, b) 2! min w,b Now: derive, build the gradient and go. Intelligent Systems: Discriminative Learning, Neural Networks 22

23 Error Back-Propagation We start from a single neuron and just one example (x, k). Remember: F (w, b) =(k y) 2 y = exp( y 0 ) y 0 = hx, wi = X j x j w j Derivation according to the (w, 0 j = =(y k) exp( y 0 ) (1 + exp( y 0 )) 2 x j = d(y 0 ) x j Intelligent Systems: Discriminative Learning, Neural Networks 23

24 Error Back-Propagation In general: compute errors at the i-th level from all -s at the (i + 1) -th level propagate the error. The Algorithm (for just one example (x, k) ): 1. Forward: compute all y and y 0 (apply the network), compute the output error n = y n k ; 2. Backward: compute errors in the intermediate levels: ij = X j 0 i+1j 0 d(y 0 i+1j 0 ) w i+1j 0 j 3. Compute the gradient and ijj 0 = ij d(y 0 ij) y i 1j 0 For many examples just sum them up. Intelligent Systems: Discriminative Learning, Neural Networks 24

25 A special case Convolutional Networks Local features convolutions with a set of predefined masks (more detailed in lectures Computer Vision ). Intelligent Systems: Discriminative Learning, Neural Networks 25

26 Convolutional Networks Yann LeCun, Koray Kavukcuoglu and Clement Farabet Convolutional Networks and Applications in Vision Intelligent Systems: Discriminative Learning, Neural Networks 26

27 Neural Networks Summary 1. Discriminative learning: learn decision strategies directly without to consider the underlying probability model at all 3. Neurons are linear classifiers. 4. Learning by the Perceptron-algorithm. 5. Many non-linear decision rules can be transformed to linear ones by the corresponding transformation of the input space. 6. Classification into more than two classes is possible either by naïve approaches (e.g. by a set of one-vs-all simple classifiers) or more principled by Fisher-Classifier. 5. Feed-Forward Neural Networks implement (arbitrary complex) decision strategies. 6. Error Back-Propagation for learning (gradient descent). 7. Some special cases are useful in Computer Vision. Intelligent Systems: Discriminative Learning, Neural Networks 27