Statistical Machine Learning from Data

Transcription

1 January 17, 2006 Samy Bengio Statistical Machine Learning from Data 1 Statistical Machine Learning from Data Multi-Layer Perceptrons Samy Bengio IDIAP Research Institute, Martigny, Switzerland, and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland bengio@idiap.ch

2 Samy Bengio Statistical Machine Learning from Data 2 1 Generalities

3 Samy Bengio Statistical Machine Learning from Data 3 1 Generalities

4 Artificial Neural Networks Samy Bengio Statistical Machine Learning from Data 4 y y = g(s) s = f(x;w) w1 w2 x1 x2 An ANN is a set of units (neurons) connected to each other Each unit may have multiple inputs but have one output Each unit performs 2 functions: integration: s = f (x; θ) transfer: y = g(s)

5 Artificial Neural Networks: Functions Samy Bengio Statistical Machine Learning from Data 5 Example of integration function: s = θ 0 + i x i θ i Examples of transfer functions: tanh: y = tanh(s) 1 sigmoid: y = 1 + exp( s) Some units receive inputs from the outside world. Some units generate outputs to the outside world. The other units are often named hidden. Hence, from the outside, an ANN can be viewed as a function. There are various forms of ANNs. The most popular is the Multi Layer Perceptron (MLP).

6 Transfer Functions (Graphical View) Samy Bengio Statistical Machine Learning from Data 6 y = tanh(x) 1 1 y = sigmoid(x) y = x 1

7 Samy Bengio Statistical Machine Learning from Data 7 Generalities Introduction Characteristics 1 Generalities

8 Introduction Characteristics (Graphical View) outputs units output layer hidden layers parameters input layer inputs Samy Bengio Statistical Machine Learning from Data 8

9 Introduction Characteristics An MLP is a function: ŷ = MLP(x; θ) The parameters θ = {w l i,j, bl i : i, j, l} From now on, let x i (p) be the i th value in the p th example represented by vector x(p) (and when possible, let us drop p). Each layer l (1 l M) is fully connected to the previous layer Integration: s l i = b l i + j y l 1 j w l i,j Transfer: yi l = tanh(si l) or y i l = sigmoid(si l) or y i l = si l The output of the zeroth layer contains the inputs yi 0 = x i The output of the last layer M contains the outputs ŷ i = yi M Samy Bengio Statistical Machine Learning from Data 9

10 Characteristics of MLPs Introduction Characteristics An MLP can approximate any continuous functions However, it needs to have at least 1 hidden layer (sometimes easier with 2), and enough units in each layer Moreover, we have to find the correct value of the parameters θ This is an NP-complete problem!!!! How can we find these parameters? Answer: optimize a given criterion using a gradient method. Note: capacity controlled by the number of parameters Samy Bengio Statistical Machine Learning from Data 10

11 Samy Bengio Statistical Machine Learning from Data 11 Separability Generalities Introduction Characteristics Linear Linear+Sigmoid Linear+Sigmoid+Linear Linear+Sigmoid+Linear+Sigmoid+Linear

12 Criterion Basics Derivation Algorithm and Example Universal Approximator Samy Bengio Statistical Machine Learning from Data 12 1 Generalities

13 Generalities Criterion Basics Derivation Algorithm and Example Universal Approximator Objective: minimize a criterion C over a set of data D n : n C(D n, θ) = L(y(p), ŷ(p)) where p=1 ŷ(p) = MLP(x(p); θ) We are searching for the best parameters θ: θ = arg min θ C(D n, θ) Gradient descent: an iterative procedure where, at each iteration s we modify the parameters θ: θ s+1 = θ s η C(D n, θ s ) θ s where η is the learning rate. WARNING: local optima. Samy Bengio Statistical Machine Learning from Data 13

14 : The Basics Criterion Basics Derivation Algorithm and Example Universal Approximator a = f(b) Chain Rule if a = f (b) and b = g(c) then a c = a b b c = f (b) g (c) b = g(c) c Samy Bengio Statistical Machine Learning from Data 14

15 : The Basics Criterion Basics Derivation Algorithm and Example Universal Approximator Sum Rule if a = f (b, c) and b = g(d) and c = h(d) a = f(b,c) then a d = a b b d + a c c d b = g(d) c = h(d) a d f (b, c) = g f (b, c) (d)+ h (d) b c d Samy Bengio Statistical Machine Learning from Data 15

16 Criterion Basics Derivation Algorithm and Example Universal Approximator Basics (Graphical View) criterion targets outputs back propagation of the error parameter to tune inputs Samy Bengio Statistical Machine Learning from Data 16

17 : Criterion Criterion Basics Derivation Algorithm and Example Universal Approximator First: we need to pass the gradient through the criterion The global criterion C is: n C(D n, θ) = L(y(p), ŷ(p)) p=1 Example: the mean squared error criterion (MSE): L(y, ŷ) = d i=1 1 2 (y i ŷ i ) 2 And the derivative with respect to the output ŷ i : L(y, ŷ) ŷ i = ŷ i y i Samy Bengio Statistical Machine Learning from Data 17

18 Samy Bengio Statistical Machine Learning from Data 18 Generalities : Last Layer Criterion Basics Derivation Algorithm and Example Universal Approximator Second: derivative wrt the parameters of the last layer M ŷ i = y M i = tanh(s M i ) s M i = b M i + j y M 1 j w M i,j Hence the derivative with respect to w M i,j is: ŷ i w M i,j = ŷ i s M i sm i w M i,j And the derivative with respect to b M i = (1 (yi M ) 2 ) y M 1 j is: ŷ i b M i = ŷ i s M i sm i b M i = (1 (y M i ) 2 ) 1

19 Samy Bengio Statistical Machine Learning from Data 19 Generalities : Other Layers Criterion Basics Derivation Algorithm and Example Universal Approximator Third: derivative wrt to the output of a hidden layer y l j ŷ i y l j = k ŷ i y l+1 k y l+1 k y l j where and y l+1 k y l j ŷ i y M i = y l+1 k s l+1 k sl+1 k yj l = (1 (y l+1 k ) 2 ) w l+1 k,j = 1 and ŷ i y M k i = 0

20 Samy Bengio Statistical Machine Learning from Data 20 Generalities Criterion Basics Derivation Algorithm and Example Universal Approximator : Other Parameters Fourth: derivative wrt the parameters of hidden layer y l j and ŷ i w l j,k ŷ i b l j = ŷ i y l j = ŷ i y l j = ŷ i y l j = ŷ i y l j y l j w l j,k y l 1 k y l j b l j 1

21 Criterion Basics Derivation Algorithm and Example Universal Approximator : Global Algorithm For each iteration 1 Initialize gradients C θ i = 0 for each θ i 2 For each example z(p) = (x(p), y(p)) 1 Forward phase: compute ŷ(p) = MLP(x(p), θ) 2 Compute L(y(p),ŷ(p)) ŷ(p) 3 For each layer l from M to 1: 1 Compute ŷ(p) y j l y l j 2 Compute y j l and b j l w j,k l 3 Accumulate gradients: C b l j = C + C bj l L C wj,k l 3 Update the parameters: θ s+1 = C + C wj,k l L L ŷ(p) ŷ(p) yj l L ŷ(p) ŷ(p) yj l = θ s i η C θ s i y l j b l j y l j w l j,k i Samy Bengio Statistical Machine Learning from Data 21

22 : An Example (1) Criterion Basics Derivation Algorithm and Example Universal Approximator Let us start with a simple MLP: 0.3 linear tanh tanh Samy Bengio Statistical Machine Learning from Data 22

23 Samy Bengio Statistical Machine Learning from Data 23 Generalities : An Example (2) Criterion Basics Derivation Algorithm and Example Universal Approximator We forward one example and compute its MSE: linear 1.07 MSE =1.23 target tanh tanh

24 Samy Bengio Statistical Machine Learning from Data 24 Generalities : An Example (3) Criterion Basics Derivation Algorithm and Example Universal Approximator We backpropagate the gradient everywhere: 0.3 db= dw= linear 1.07 MSE =1.23 target 0.5 dy = 1.57 dmse =1.57 ds = dw= dy= dy= tanh tanh db= ds= ds= dw=0.53 dw= dw= 0.53 dw= db=

25 : An Example (4) Criterion Basics Derivation Algorithm and Example Universal Approximator We modify each parameter with learning rate 0.1: 0.1 linear tanh tanh Samy Bengio Statistical Machine Learning from Data 25

26 Samy Bengio Statistical Machine Learning from Data 26 Generalities : An Example (5) Criterion Basics Derivation Algorithm and Example Universal Approximator We forward the same example and compute its (smaller) MSE: linear 0.24 MSE =0.27 target tanh tanh

27 Samy Bengio Statistical Machine Learning from Data 27 Generalities MLP are Universal Approximators Criterion Basics Derivation Algorithm and Example Universal Approximator It can be shown that, under reasonable assumptions, one can approximate any smooth function with an MLP with one layer of hidden units. First intuition: Let us consider a classification task Let us consider hard transfert functions for hidden units: { 1 if s > 0 y = step(s) = 0 otherwise Let us consider linear transfert functions for output units: First attempt: ŷ = c + y = s N M v i sign x j w i,j + b i i=1 j=1

28 Criterion Basics Derivation Algorithm and Example Universal Approximator Illustration: Universal Approximators Samy Bengio Statistical Machine Learning from Data 28

29 Criterion Basics Derivation Algorithm and Example Universal Approximator Illustration: Universal Approximators Samy Bengio Statistical Machine Learning from Data 29

30 Criterion Basics Derivation Algorithm and Example Universal Approximator Illustration: Universal Approximators Samy Bengio Statistical Machine Learning from Data 30

31 Criterion Basics Derivation Algorithm and Example Universal Approximator Illustration: Universal Approximators Samy Bengio Statistical Machine Learning from Data 31

32 Criterion Basics Derivation Algorithm and Example Universal Approximator Illustration: Universal Approximators... but what about that? Samy Bengio Statistical Machine Learning from Data 32

33 Samy Bengio Statistical Machine Learning from Data 33 Generalities Criterion Basics Derivation Algorithm and Example Universal Approximator Universal Approximation by Cosines Let us consider simple functions of two variables y(x 1, x 2 ) Fourrier decomposition: y(x 1, x 2 ) s A s (x 1 ) cos(sx 2 ) where coefficients of A s are functions of x 1. Further Fourrier decomposition: y(x 1, x 2 ) A s,l cos(lx 1 ) cos(sx 2 ) s l We know that cos(α) cos(β) = 1 2 cos(α + β) cos(α β): y(x 1, x 2 ) [ 1 A s,l 2 cos(lx 1 + sx 2 ) + 1 ] 2 cos(lx 1 sx 2 ) s l

34 Criterion Basics Derivation Algorithm and Example Universal Approximator Universal Approximation by Cosines The cos function can be approximated with linear combinations of step functions: f (z) = f 0 + i (f i+1 f i )step(z z i ) So y(x 1, x 2 ) can be approximated by a linear combination of step functions whose arguments are linear combinations of x 1 and x 2, and which can be approximated by tanh functions. Samy Bengio Statistical Machine Learning from Data 34

35 Samy Bengio Statistical Machine Learning from Data 35 Generalities Binary Multiclass Error Correcting Output Codes 1 Generalities

36 ANN for Binary Binary Multiclass Error Correcting Output Codes One output with target coded as { 1, 1} or {0, 1} depending on the last layer output function (linear, sigmoid, tanh,...) For a given output, the associated class corresponds to the nearest target. How to obtain class posterior probabilities: use a sigmoid with targets {0, 1} if the model is correctly trained (with, for instance MSE criterion) = ŷ(x) = E[Y X = x] = 1 P(Y = 1 X = x)+0 P(Y = 0 X = x) the output will thus encode P(Y = 1 X = x) Note: we do not optimize directly the classification error... Samy Bengio Statistical Machine Learning from Data 36

37 Samy Bengio Statistical Machine Learning from Data 37 Generalities ANN for Multiclass Binary Multiclass Error Correcting Output Codes Simplest solution: one-hot encoding One output per class, coded for instance as (0,, 1,, 0) For a given output, the associated class corresponds to the index of the maximum value in the output vector How to obtain class posterior probabilities: use a softmax: ŷ i = exp(s i) X exp(s j ) j each output i will encode P(Y = i X = x) Otherwise: each class corresponds to a different binary code For example for a 4-class problem, we could have an 8-dim code for each class For a given output, the associated class corresponds to the nearest code (according to a given distance) Example: Error Correcting Output Codes (ECOC)

38 Error Correcting Output Codes Binary Multiclass Error Correcting Output Codes Let us represent a 4-class problem with 6 bits: class 1: class 2: class 3: class 4: We then create 6 classifiers (or 1 classifier with 6 outputs) For example: the first classifier will try to separate classes 1 and 2 from classes 3 and 4 Samy Bengio Statistical Machine Learning from Data 38

39 Error Correcting Output Codes Binary Multiclass Error Correcting Output Codes Given our 4-class problem represented with 6 bits: class 1: class 2: class 3: class 4: When a new example comes, we compute the distance between the code obtained by the 6 classifiers and the 4 classes: obtained: distances: (let us use Manhatan distance) to class 1: 5 to class 3: 2 to class 2: 4 to class 4: 3 Samy Bengio Statistical Machine Learning from Data 39

40 Binary Multiclass Error Correcting Output Codes What is a Good Error Correcting Output Code How to devise a good error correcting output code? Maximize the minimum Hamming distance between any pair of code words. A good ECOC should satisfy two properties: Row separation. (Hamming distance) Column separation. Column functions should be as uncorrelated as possible with each other. Samy Bengio Statistical Machine Learning from Data 40

41 Stochastic Initialization Learning Rate Weight Decay Training Criteria Samy Bengio Statistical Machine Learning from Data 41 1 Generalities

42 Generalities Stochastic Initialization Learning Rate Weight Decay Training Criteria A good book to make ANNs working Content: G. B. Orr and K. Müller. Neural Networks: Tricks of the Trade Springer. Stochastic Gradient Initialization Learning Rate and Learning Rate Decay Weight Decay Samy Bengio Statistical Machine Learning from Data 42

43 Stochastic Stochastic Initialization Learning Rate Weight Decay Training Criteria The gradient descent technique is batch: First accumulate the gradient from all examples, then adjust the parameters What if the data set is very big, and contains redundencies? Other solution: stochastic gradient descent Adjust the parameters after each example instead Stochastic: we approximate the full gradient with its estimate at each example Nevertheless, convergence proofs exist for such method. Moreover: much faster for large data sets!!! Other gradient techniques: second order methods such as conjugate gradient: good for small data sets Samy Bengio Statistical Machine Learning from Data 43

44 Samy Bengio Statistical Machine Learning from Data 44 Initialization Generalities Stochastic Initialization Learning Rate Weight Decay Training Criteria How should we initialize the parameters of an ANN? One common problem: saturation When the weighted sum is big, the output of the tanh (or sigmoid) saturates, and the gradient tends towards 0 derivative is good derivative is almost zero weighted sum

45 Samy Bengio Statistical Machine Learning from Data 45 Initialization Generalities Stochastic Initialization Learning Rate Weight Decay Training Criteria Hence, we should initialize the parameters such that the average weighted sum is in the linear part of the transfer function: See Leon Bottou s thesis for details input data: normalized with zero mean and unit variance, targets: regression: normalized with zero mean and unit variance, classification: output transfer function is tanh: 0.6 and -0.6 output transfer function is sigmoid: 0.8 and 0.2 output transfer function is linear: 0.6 and -0.6 [ ] 1 parameters: uniformly distributed in, 1 fan in fan in

46 Stochastic Initialization Learning Rate Weight Decay Training Criteria Learning Rate and Learning Rate Decay How to select the learning rate η? If η is too big: the optimization diverges If η is too small: the optimization is very slow and may be stuck into local minima One solution: progressive decay initial learning rate η = η 0 learning rate decay η d At each iteration s: η(s) = η 0 (1 + s η d ) Samy Bengio Statistical Machine Learning from Data 46

47 Stochastic Initialization Learning Rate Weight Decay Training Criteria Learning Rate Decay (Graphical View) /(1+0.1*x) Samy Bengio Statistical Machine Learning from Data 47

48 Samy Bengio Statistical Machine Learning from Data 48 Weight Decay Generalities Stochastic Initialization Learning Rate Weight Decay Training Criteria One way to control the capacity: regularization For MLPs, when the weights tend to 0, sigmoid or tanh functions are almost linear, hence with low capacity Weight decay: penalize solutions with high weights and bias (in amplitude) C(D n, θ) = n L(y(p), ŷ(p)) + β θ 2 p=1 where β controls the weight decay. Easy to implement: n = θj s θ s+1 j p=1 j=1 θ 2 j L(y(p), ŷ(p)) η θj s η β θj s

49 Examples of Training Criteria Stochastic Initialization Learning Rate Weight Decay Training Criteria Mean-squared error, for regression: L(y, ŷ) = d i=1 1 2 (y i ŷ i ) 2 Cross-entropy criterion, for classification (targets { 1, 1}): Hard version: L(y, ŷ) = log(1 + exp( y i ŷ i )) L(y, ŷ) = 1 y i ŷ i + Samy Bengio Statistical Machine Learning from Data 49

50 Examples of Training Criteria Stochastic Initialization Learning Rate Weight Decay Training Criteria log(1 + exp(-x)) 1 - x + 0.5*x*x Samy Bengio Statistical Machine Learning from Data 50