111 lines
3.8 KiB
Mathematica
111 lines
3.8 KiB
Mathematica
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classdef ElasticNodes < handle
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%ELASTICNODES It encapsulate global variables necessary for width
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%adaptation
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%
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% This class enabless elastic network width. Network width adaptation
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% supports automatic generation of new hidden nodes and prunning of
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% inconsequential nodes. This mechanism is controlled by the NS
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% (Network Significance) method which estimates the network
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% generalization power in terms of bias and variance
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properties (Access = public)
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growable; % See full comment below
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% Hold an array of boolean elements indicating if that layer can
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% receive grow or not during width adaptation procedure
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prunable; % See full comment below
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% Hold an array of integer elements indicating if that layer can
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% receive prune or not during width adaptation procedure.
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% 0 indicates that no node should be pruned. Anything different
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% than zero indicantes which node should be pruned in that layer.
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end
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properties (Access = public)
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dataMean = 0;
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dataStd = 0;
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dataVar = 0;
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nSamplesFeed = 0;
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nSamplesLayer;
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% NS = Network Significance
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%BIAS VARIABLES
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meanBIAS;
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varBIAS;
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stdBIAS;
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minMeanBIAS;
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minStdBIAS;
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BIAS2;
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%VAR VARIABLES
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meanVAR;
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varVAR;
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stdVAR;
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minMeanVAR;
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minStdVAR;
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VAR;
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% metrics
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nodeEvolution = {}; % TODO: Need to include at the grow/prune part
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end
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%% Evolving layers properties
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properties (Access = public)
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alpha = 0.005;
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gradientBias = [];
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meanNetBias2;
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meanNetVar;
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end
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methods (Access = protected)
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function self = ElasticNodes(nHiddenLayers)
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nhl = nHiddenLayers; % readability
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self.nSamplesLayer = zeros(1,nhl);
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self.meanBIAS = zeros(1,nhl);
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self.varBIAS = zeros(1,nhl);
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self.stdBIAS = zeros(1,nhl);
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self.minMeanBIAS = ones(1,nhl) * inf;
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self.minStdBIAS = ones(1,nhl) * inf;
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self.BIAS2 = num2cell(zeros(1,nhl));
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self.meanVAR = zeros(1,nhl);
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self.varVAR = zeros(1,nhl);
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self.stdVAR = zeros(1,nhl);
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self.minMeanVAR = ones(1,nhl) * inf;
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self.minStdVAR = ones(1,nhl) * inf;
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self.VAR = num2cell(zeros(1,nhl));
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self.growable = zeros(1,nhl);
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% self.prunable = zeros(1,nhl);
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self.prunable = cell(1,nhl);
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for i = 1 : nhl
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self.prunable{i} = 0;
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end
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end
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function growLayerEvolutiveParameter(self, numberHiddenLayers)
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nhl = numberHiddenLayers; %readability
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self.nSamplesLayer = [self.nSamplesLayer, 0];
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self.meanBIAS = [self.meanBIAS, 0];
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self.varBIAS = [self.varBIAS, 0];
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self.stdBIAS = [self.stdBIAS, 0];
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self.minMeanBIAS = [self.minMeanBIAS, 0];
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self.minStdBIAS = [self.minStdBIAS, 0];
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self.BIAS2 = [self.BIAS2, 0];
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self.meanVAR = [self.meanVAR, 0];
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self.varVAR = [self.varVAR, 0];
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self.stdVAR = [self.stdVAR, 0];
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self.minMeanVAR = [self.minMeanVAR, 0];
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self.minStdVAR = [self.minStdVAR, 0];
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self.VAR = [self.VAR, 0];
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self.growable = zeros(1, nhl + 1);
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self.prunable = cell(1, nhl + 1);
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for i = 1 : nhl + 1
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self.prunable{i} = 0;
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end
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end
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end
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end
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