Multilayer Feedforward Networks. Berlin Chen, 2002

Transcription

1 Multilayer Feedforard Netors Berlin Chen, 00

2 Introduction The single-layer perceptron classifiers discussed previously can only deal ith linearly separable sets of patterns The multilayer netors to be introduced here are the most idespread neural netor architecture Made useful until the 980s, because of lac of efficient training algorithms (McClelland and Rumelhart 986)

3 Introduction Supervised Error Bac-propagation Training The mechanism of bacard error transmission (delta learning rule) is used to modify the synaptic eights of the internal (hidden) and output layers The mapping error can be propagated into hidden layers Can implement arbitrary comple/output mappings or decision surfaces for to separate pattern classes For hich, the eplicit derivation of mappings and discovery of relationships is almost impossible Produce surprising results and generalizations 3

4 Linearly Non-separable Pattern Classification Linearly non-separable dichotomization For to training sets C and C of the augmented patterns, if no eight vector eists such that t y > 0 for each y C t y < 0 for each y C Then the patterns set C and C are linearly non-separable To-dimensional Input pattern space Three-dimensional Input pattern space 4

5 Linearly Non-separable Pattern Classification Map the patterns in the original pattern space into the so-called image space such that a tolayer netor can classify them -dimensional pattern space 3-dimensional image space (-,,-) (-,,) (,,-) -,-,) (,-,) o 4 sgn sgn ( o + o + o 3 ) ( o + o + o ) 3 > < 0 : 0 : class class 5

6 Linearly Non-separable Pattern Classification Patterns mapped into the three-dimensional cube Produce linearly separable images in the image space o 4 sgn sgn ( o + o + o 3 ) ( o + o + o ) 3 > < 0 : 0 : class class 6

7 Linearly Non-separable Pattern Classification Eample 4.: the XOR function using a simple layered classifier (ith parameters produced by inspection) + 0 o sgn Output o sgn bipolar discrete perceptron 7

8 Eample 4. Linearly Non-separable Pattern Classification The mapping using discrete perceptrons o sgn + The mapping using continuous perceptrons o sgn Contour map o 3 (, ) 8

9 Linearly Non-separable Pattern Classification Another eample: classification of planner patterns For class, only one set of them ill be both activated at the same time 9

10 Linearly Non-separable Pattern Classification The layered netors ith discrete perceptrons described here are also called committee netor Committee Voting Input pattern space Image space Class membership [, -] N A verte of cube 0

11 Error Bac-propagation Training for Multi-layer Feed-forard Netors The error bac-propagation training algorithm has reaaed the scientific and engineering community to the modeling of many quantitative phenomena using neural netors The Delta Learning Rule is applied Each neuron has a nonlinear and differentiable activation function (sigmoid function) Neurons (synaptic) eights are adusted based on the least mean square (LMS) criterion

12 Error Bac-propagation Training for Multi-layer Feed-forard Netors Training: eperiential acquisition of input/output mapping noledge ithin multilayer netors Input patterns submitted sequentially during training Synaptic eights and thresholds adusted to reduce the mean square classification error The eight adustments enforce bacard from the output layer through the hidden layers toard the input layer Continued until the netor are ithin an acceptable overall error for the hole training set

13 Error Bac-propagation Training for Multi-layer Feed-forard Netors Revisit the Delta Learning Rule for the singlelayer netor Continuous activation functions Gradient descent search [ Wy], net Wy o Γ The Derivation for A Specific Neuron E K ( ) ( ) J d o, o f net f y + E η η negative gradient decent formula ( d o ) f ( net ) y y J 3

14 Error Bac-propagation Training for Multi-layer Feed-forard Netors Revisit the Delta Learning Rule for the singlelayer netor The definition of the error signal term δ o E ( net ) E ( net ) E ( net ) ( net ) δ o δ y o f δ for a specific neuron E ( net ) E η ηδ f J y f ( net ) y f E ( net ) net ( net ) net f + ηδ ( d o ) o y ( net ) o y 4

15 Error Bac-propagation Training for Multi-layer Feed-forard Netors Revisit the Delta Learning Rule for the singlelayer netor Unipolar continuous activation function f ( net ) f + ep ( net ) ( d o ) o ( o ) y η ( net ) o ( o ) Bipolar continuous activation function f ( net ) + η ep δ o ( net ) f ( )( d ) o o y δ o ( net ) ( o ) 5

16 6 Error Bac-propagation Training for Multi-layer Feed-forard Netors Revisit the Delta Learning Rule for the singlelayer netor are local error signals dependent only on and t δy W W η + KJ K K J J W ok o o δ δ δ.. δ [ ] J t y y y.. y o δ o d

17 Error Bac-propagation Training for Multi-layer Feed-forard Netors Generalized Delta learning rule for hidden Layers 7

18 Error Bac-propagation Training for Multi-layer Feed-forard Netors Apply the negative gradient decent formula for the hidden layer E v i η net I v i z v δ v y v i i E η net ηδ E y z i i net v The error signal term of the hidden layer net having output y i i i 8

19 9 Error Bac-propagation Training for Multi-layer Feed-forard Netors Apply the negative gradient decent formula for the hidden layer y net y y E δ [ ] ( ) [ ] J K K y net net f d o d E E δ o ( ) [ ] ( ) ( ) [ ] ( ) { } K K net f net f d y net net net f d y net net E y net net E y E

20 Error Bac-propagation Training for Multi-layer Feed-forard Netors Generalized Delta learning rule for hidden Layers y y f net δ y f f ( ) net f ( net ) ( ) K net [( ) ( ) ] d o f net ( net ) K δ o v v i i v ηδ i + y z i v i v i + η f ( ) K net z i δ o 0

21 Error Bac-propagation Training for Multi-layer Feed-forard Netors Generalized Delta learning rule for hidden Layers Bipolar continuous activation function v i v v i i + η f + η K ( net ) z [( d o ) f ( net ) ] K ( y ) z ( d o )( o ) i i Unipolar continuous activation function v i v v i i + η f + η y K ( net ) z [( d o ) f ( net ) ] K ( y ) z [( d o ) o ( o ) ] i i The adustment of eights leading to neuron in the hidden layer is proportional to the eighted sum of all δvalues at the adacent folloing layer of nodes connecting neuron ith the output

22 Error Bac-propagation Training for Multi-layer Feed-forard Netors Bipolar continuous activation function

23 Error Bac-propagation Training for Multi-layer Feed-forard Netors o Γ [ W Γ [ V z ] 3

24 Error Bac-propagation Training for Multi-layer Feed-forard Netors The incremental learning of the eight matri in the output and hidden layers is obtained by the outer product rule as W η δy t δ Where is the error signal vector of a layer and is the input signal to that layer y The netor is nonlinear in the feedforard mode, hile the error bac-propagation is computed using the linearized activation The slope of each neuron s activation function 4

25 Eamples of Error Bac-Propagation Training Eample 4.: XOR function o o Output W V [ ]

26 Eamples of Error Bac-Propagation Training Eample 4.: XOR function The first sample run ith random initial eight values 44 steps (η0.) W V [ ] Though continuous neurons are used for training, e replace them ith bipolar binary neurons 6

27 Eamples of Error Bac-Propagation Training Eample 4.: XOR function The second sample run ith random initial eight values 8 steps (η0.) W [ ] V If the netor has failed to learn the training set successfully, the training should be restarted ith Different initial eights 7

28 Training Errors For the purpose of assessing the quality and success of training, the oint error (cumulative error) must be computed for the entire batch of training patterns E P p ( d ) It is not very useful for comparison of netors ith different numbers of training patterns and having different number of output neurons Root-mean-square normalized error E rms PK K P p o p K p ( d ) p o p 8

29 Training Errors For some classification applications The desired outputs belo a threshold ill be set to 0, hile the desired outputs higher than an other threshold ill be set to o o In such cases, the decision error ill more adequately reflects the accuracy of neural netor classifiers E p p < > d N PK err o o The netors in classification applications may ehibit zero decision errors hile still yielding substantial E and E rms p p 0 Average number of bit errors 9

30 Multilayer Feedforard Netors as Function Approimators Eample: a function h() approimated by H(,) 30

31 Multilayer Feedforard Netors as Function Approimators { } There are P samples,,..., p, hich are eamples of function values in the interval (a, b) b a i+ i, for i,..., P Each subinterval ith length is P i, i +, i,,..., P a, P + b 3

32 3 Multilayer Feedforard Netors as Function Approimators Define a unit step function Use a staircase approimation H(,) of the continuous-valued function h() The netor ill have P binary (nonlinear) perceptrons ith TLUs in the input layer ( ) > < + 0 for 0 for undefined 0 for 0 ) sgn( ζ ( ) , h h H P P P i i ζ ζ ζ ζ

33 Multilayer Feedforard Netors as Function Approimators If e replace the TLUs ith continuous activation functions bump function (may not the best case for a particular problem) 33

34 Multilayer Feedforard Netors as Function Approimators The output layer in the above eample also can be replaced ith a preceptron ith nonlinear activation function Such a netor architecture can approimate virtually any multivariable function, if provided sufficiently many hidden neurons are available 34

35 Learning Factors Error Curve 35

36 Learning Factors Initial Weights The eights of the netor are typically initialized at small random values The initialization strongly affects the ultimate solution Equal initial eights? Select another set of initial eights, and then restart! Incremental Updating versus Cumulative Weight Adustment Incremental Updating: Weight adustments do not need to be stored May seed toard the most recent patterns in the training cycle 36

37 Learning Factors Incremental Updating versus Cumulative Weight Adustment Cumulative Weight Adustment : P p p Provided that the learning constant is small enough, the cumulative eight adustment procedure can still implement the algorithm close to the gradient decent minimization We may present the training eamples in random in each training cycle 37

38 Learning Factors f f Steepness of the activation function ( net ) ( net ) + ep net ( λ ) λ ep ( λ net ) [ + ep ( λ net )] Bipolar continuous activation function Have a maimum value of λ at net0 The large λ may yield results similar to that of large learning constant η 38

39 Learning Factors Momentum Method Supplement the current eight adustments ith a fraction of the most recent eight adustment ( t ) η E ( t ) + α ( t ) After a total of N steps ith the momentum method N () n t η α E ( t n ) n 0 39

40 Learning Factors Momentum Method 40

41 Summary of Error Bac-propagation Netor A set of P training pairs (z p,d p ) {( z, d ), p, P} p p,..., Minimize the vector of total error E P p o ( W, V, z ) p d p 4

42 Netor Architecture vs. Data Representation... 9 t [ ] t [ ] : class C,T : class C,I,T t [ 3 3] : class C,I, T 4

43 Necessary Number of Hidden Neurons For to-layer feedforard netor if the n-dimensional nonargumented input space is linear separable into M disoint regions, the necessary number of hidden neuons ould be J M J Mirchandini and Cao (989) 43

44 Character Recognition Application Proect a point of the character into its three closest vertical, horizontal, and diagonal bars Then normalized the bar values to be beteen 0 and Input vector is 3-dimensional and the activation function is unipolar continuous function 90~95% accuracy I J K

45 Character Recognition Application Eample

46 Digit Recognition Application 96~98% accuracy 46

47 Epert System Applications Eplanation function: Neural netor epert systems are typically unable to provide the user ith the reasons for the decisions made. 47

48 Learning Time Sequences 48

49 Functional Lin Netor Enhance the representation of the input data Any to elements Any three elements 49