jens
549089a440
git-svn-id: http://moon:8086/svn/software/trunk/projects/RBM@45 b431acfa-c32f-4a4a-93f1-934dc6c82436
695 lines
14 KiB
C++
695 lines
14 KiB
C++
/*
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* Rbm.hpp
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*
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* Created on: 21.09.2014
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* Author: jens
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*/
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#ifndef RBM_HPP_
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#define RBM_HPP_
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#include "VisibleLayer.hpp"
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#include "HiddenLayer.hpp"
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#include "Weights.hpp"
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#include <cmath>
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#include <Eigen/Dense>
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using namespace Eigen;
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void mylog(const char* format, ...);
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#define printf mylog
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class Rbm;
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class RbmListener
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{
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public:
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RbmListener() {}
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virtual ~RbmListener()
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{
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}
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virtual void onEpochTrained(const Rbm &obj) = 0;
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};
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class Rbm
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{
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public:
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Rbm(Weights &weights, RbmListener *pListener = nullptr)
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: m_w(weights)
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, m_pListener(pListener)
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, m_progress(0)
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, m_sigma(1.0)
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, m_sigmaDecay(1.0)
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, m_weightDecay(0.0)
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, m_lambda(1.0)
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, m_sparsity(0)
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, m_muWeights(0.01)
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, m_muSparsity(0.01)
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, m_momentum(0.5)
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, m_doCancel(false)
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, m_useVisibleGaussian(false)
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, m_doRaoBlackwell(false)
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, m_useProbsForHiddenReconstruction(false)
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, m_doSparse(false)
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, m_doNormalizeData(false)
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, m_numGibbs(1)
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{
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Noise_Init(&m_noise, 0x32727155);
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}
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~Rbm()
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{
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cancel();
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Noise_Free(&m_noise);
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}
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void train(const LayerArray<VisibleLayer> &batch, uint32_t numEpochs, double sigmaMin = 0.05)
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{
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uint32_t t, i;
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uint32_t epoch;
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uint32_t gibbs;
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double sigma;
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VisibleLayer v(m_w.getNumVisible());
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HiddenLayer h(m_w.getNumHidden());
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VectorXd sumBiasV(m_w.getNumVisible());
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VectorXd deltaBiasV(m_w.getNumVisible());
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VectorXd sumBiasH(m_w.getNumHidden());
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VectorXd deltaBiasH(m_w.getNumHidden());
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MatrixXd sumWeights(m_w.getNumVisible(), m_w.getNumHidden());
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MatrixXd deltaWeights(m_w.getNumVisible(), m_w.getNumHidden());
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MatrixXd diffErr(1, m_w.getNumVisible());
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const LayerArray<VisibleLayer> &vt = batch;
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sigma = m_sigma;
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double dProgress = 1.0/numEpochs;
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double kTrain = 1.0/vt.getSize();
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m_progress = 0;
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deltaWeights.fill(0);
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deltaBiasV.fill(0);
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deltaBiasH.fill(0);
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m_doCancel = false;
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for (epoch=0; epoch < numEpochs; epoch++)
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{
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double err = 0;
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if (m_doCancel)
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{
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m_doCancel = false;
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break;
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}
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sumWeights.fill(0);
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sumBiasV.fill(0);
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sumBiasH.fill(0);
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for (i=0; i < vt.getSize(); i++)
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{
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t = i;
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h.probsUpdateLogistic(vt[t], m_w, m_lambda, sigma);
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// Create hidden layer base on training data
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if (m_doRaoBlackwell)
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{
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h.states() = h.probs();
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}
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else
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{
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h.statesUpdateStochastic();
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}
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// Update weights (positive phase)
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sumWeights += vt[t].states() * h.states().transpose();
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sumBiasV += vt[t].states();
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sumBiasH += h.states();
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diffErr = vt[t].states();
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for (gibbs=0; gibbs < m_numGibbs; gibbs++)
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{
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h.statesUpdateStochastic();
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// Create visible reconstruction (a fantasy...)
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if (m_useProbsForHiddenReconstruction)
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{
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if (m_useVisibleGaussian)
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{
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v.probsUpdateGaussian(h, m_w, m_lambda, sigma);
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}
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else
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{
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v.probsUpdateLogistic(h, m_w, m_lambda, sigma);
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}
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v.states() = v.probs();
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}
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else
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{
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if (m_useVisibleGaussian)
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{
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v.sampleGaussian(h, m_w, m_lambda, sigma);
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}
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else
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{
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v.probsUpdateLogistic(h, m_w, m_lambda, sigma);
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v.statesUpdateStochastic();
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}
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}
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// Create hidden reconstruction
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h.probsUpdateLogistic(v, m_w, m_lambda, sigma);
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}
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// Update weights (negative phase)
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if (m_doRaoBlackwell)
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{
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h.states() = h.probs();
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}
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else
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{
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h.statesUpdateStochastic();
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}
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sumWeights -= v.states() * h.states().transpose();
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sumBiasV -= v.states();
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sumBiasH -= h.states();
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diffErr -= v.states();
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diffErr.array() *= diffErr.array();
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err += diffErr.sum();
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} // TrainingSize
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deltaWeights = m_momentum*deltaWeights + m_muWeights*(kTrain*sumWeights - m_weightDecay*m_w.weights());
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m_w.weights() += deltaWeights;
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deltaBiasV = m_momentum*deltaBiasV + m_muWeights*kTrain*sumBiasV;
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m_w.visibleBias() += deltaBiasV;
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if (m_doSparse)
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{
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HiddenLayer th(m_w.getNumHidden());
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VectorXd m(m_w.getNumHidden());
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m.fill(0);
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for (i=0; i < vt.getSize(); i++)
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{
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th.probsUpdateLogistic(vt[i], m_w, m_lambda, sigma);
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m += th.probs();
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}
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m /= i;
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sumBiasH = m_sparsity - m.array();
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deltaBiasH = m_momentum*deltaBiasH + m_muSparsity*sumBiasH;
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cout << "Mean(" << m_sparsity << ") = " << (double)m.array().mean() << endl;
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cout << m << endl;
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}
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else
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{
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deltaBiasH = m_momentum*deltaBiasH + m_muWeights*kTrain*sumBiasH;
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}
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m_w.hiddenBias() += deltaBiasH;
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if (sigma > sigmaMin)
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{
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sigma *= m_sigmaDecay;
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}
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m_progress += dProgress;
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if (m_pListener)
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{
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m_pListener->onEpochTrained(*this);
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}
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cout << "err =" << endl;
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cout << err << endl;
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} // Number of epochs
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}
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void sample(MatrixXd &src)
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{
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uint32_t i;
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for (i=0; i < src.array().size(); i++)
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{
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src.array()(i) = (double)(src.array()(i) > Noise_Uniform(&m_noise));
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}
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}
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void probsLogistic(MatrixXd &src, double lambda, double sigma)
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{
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double var = sigma*sigma;
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src.array() *= -lambda/var;
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src.array() = src.array().exp();
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src.array() += 1;
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src.array() = 1.0/src.array();
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}
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void sampleGaussian(MatrixXd &src, double lambda, double sigma)
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{
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uint32_t i;
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for (i=0; i < src.array().size(); i++)
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{
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src.array()(i) = sigma*Noise_Gaussian(&m_noise) + lambda*src.array()(i);
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}
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}
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void normalizeData(MatrixXd &src, double mu, double sigma)
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{
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uint32_t i;
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uint32_t size = src.rows();
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double mean;
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double stdDev;
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for (i=0; i < size; i++)
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{
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mean = src.row(i).array().mean();
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src.row(i).array() -= mean;
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src.row(i).array() += mu;
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}
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for (i=0; i < size; i++)
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{
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src.row(i).array() *= src.row(i).array();
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}
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for (i=0; i < size; i++)
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{
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stdDev = sqrt(src.row(i).array().mean());
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src.row(i).array() /= stdDev;
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src.row(i).array() *= sigma;
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}
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}
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void train2(const LayerArray<VisibleLayer> &vt, uint32_t numEpochs, uint32_t batchSize, double sigmaMin = 0.05)
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{
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uint32_t t, i;
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uint32_t epoch;
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uint32_t gibbs;
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double sigma = m_sigma;
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double dProgress = 1.0/numEpochs;
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double kTrain = 1.0/vt.getSize();
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// if (batchSize > vt.getSize())
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batchSize = vt.getSize();
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MatrixXd v(batchSize, m_w.getNumVisible());
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MatrixXd h(batchSize, m_w.getNumHidden());
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MatrixXd batch(batchSize, m_w.getNumVisible());
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MatrixXd sumBiasV(1, m_w.getNumVisible());
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MatrixXd sumBiasH(1, m_w.getNumHidden());
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MatrixXd sumWeights(m_w.getNumVisible(), m_w.getNumHidden());
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MatrixXd deltaBiasV(MatrixXd::Zero(1, m_w.getNumVisible()));
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MatrixXd deltaBiasH(MatrixXd::Zero(1, m_w.getNumHidden()));
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MatrixXd deltaWeights(MatrixXd::Zero(m_w.getNumVisible(), m_w.getNumHidden()));
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MatrixXd diffErr(batchSize, m_w.getNumVisible());
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m_progress = 0;
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m_doCancel = false;
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for (i=0; i < batchSize; i++)
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{
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// t = (uint32_t)(0.5 + (vt.getSize()-1)*Noise_Uniform(&m_noise));
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batch.row(i) = vt[i].states();
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}
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if (m_doNormalizeData)
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{
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normalizeData(batch, 0.0, m_sigma);
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}
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for (epoch=0; epoch < numEpochs; epoch++)
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{
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double err;
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v = batch;
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if (m_doCancel)
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{
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m_doCancel = false;
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break;
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}
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// Create hidden layer base on training data
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h = v * m_w.weights();
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h += m_w.hiddenBias().transpose().replicate(batchSize, 1);
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probsLogistic(h, m_lambda, sigma);
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if (!m_doRaoBlackwell)
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{
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sample(h);
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}
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// Update weights (positive phase)
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sumBiasV = v.colwise().sum();
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if (!m_doSparse)
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{
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sumBiasH = h.colwise().sum();
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}
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sumWeights = v.transpose() * h;
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diffErr = v;
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for (gibbs=0; gibbs < m_numGibbs; gibbs++)
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{
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sample(h);
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// Create visible reconstruction (a fantasy...)
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v = h * m_w.weights().transpose();
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v += m_w.visibleBias().transpose().replicate(batchSize, 1);
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if (m_useVisibleGaussian)
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{
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sampleGaussian(v, m_lambda, sigma);
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}
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else
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{
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probsLogistic(v, m_lambda, sigma);
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if (!m_useProbsForHiddenReconstruction)
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{
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sample(v);
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}
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}
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// Create hidden reconstruction
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h = v * m_w.weights();
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h += m_w.hiddenBias().transpose().replicate(batchSize, 1);
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probsLogistic(h, m_lambda, sigma);
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}
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if (!m_doRaoBlackwell)
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{
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sample(h);
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}
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// Update weights (negative phase)
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sumBiasV -= v.colwise().sum();
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if (!m_doSparse)
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{
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sumBiasH -= h.colwise().sum();
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}
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sumWeights -= v.transpose() * h;
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diffErr -= v;
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deltaWeights = m_momentum*deltaWeights + m_muWeights*(kTrain*sumWeights - m_weightDecay*m_w.weights());
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m_w.weights() += deltaWeights;
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deltaBiasV = m_momentum*deltaBiasV + m_muWeights*kTrain*sumBiasV;
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m_w.visibleBias() += deltaBiasV;
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if (m_doSparse)
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{
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h = v * m_w.weights();
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h += m_w.hiddenBias().transpose().replicate(batchSize, 1);
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probsLogistic(h, m_lambda, sigma);
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sumBiasH.fill(m_sparsity);
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sumBiasH -= h.colwise().mean();
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deltaBiasH = m_momentum*deltaBiasH + m_muSparsity*sumBiasH;
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// cout << "Mean(" << m_sparsity << ") = " << (double)sumBiasH.array().mean() << endl;
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// cout << sumBiasH << endl;
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}
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else
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{
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deltaBiasH = m_momentum*deltaBiasH + m_muWeights*kTrain*sumBiasH;
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}
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m_w.hiddenBias() += deltaBiasH;
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if (sigma > sigmaMin)
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{
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sigma *= m_sigmaDecay;
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}
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m_progress += dProgress;
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if (m_pListener)
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{
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m_pListener->onEpochTrained(*this);
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}
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diffErr.array() *= diffErr.array();
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err = diffErr.colwise().sum().sum();
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cout << "err =" << endl;
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cout << err << endl;
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} // Number of epochs
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}
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double getProgress() const
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{
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return m_progress;
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}
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double getEnergy(const VectorXd& visible, const VectorXd& hidden)
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{
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double energy;
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energy = m_w.visibleBias().transpose() * visible;
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energy += m_w.hiddenBias().transpose() * hidden;
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energy += visible.transpose() * m_w.weights() * hidden;
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return -energy/(m_sigma*m_sigma);
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}
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void prob(LayerArray<VisibleLayer> &vts)
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{
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uint32_t i, j;
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double z;
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double p;
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HiddenLayer *h = new HiddenLayer[vts.getSize()];
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// Create hidden layer activations based on training data
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for (j=0; j < vts.getSize(); j++)
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{
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h[j].setNumUnits(m_w.getNumHidden());
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h[j].probsUpdateLogistic(vts.getAt(j), m_w, m_lambda, m_sigma);
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// h[j].statesAssignfromProbs();
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h[j].statesUpdateStochastic();
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}
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printf("pi(t) = (pi^, v>)\n");
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for (j=0; j < vts.getSize(); j++)
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{
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cout << h[j].probs() << endl;
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}
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cout << endl;
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printf("si(t) = (si^, v>)\n");
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for (j=0; j < vts.getSize(); j++)
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{
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cout << h[j].states() << endl;
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}
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cout << endl;
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printf("p(v) = (t^, v>)\n");
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for (i=0; i < vts.getSize(); i++)
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{
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z = 0;
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for (j=0; j < vts.getSize(); j++)
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{
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z += exp(-getEnergy(vts.getAt(j).states(), h[i].states()));
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}
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for (j=0; j < vts.getSize(); j++)
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{
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p = exp(-getEnergy(vts.getAt(j).states(), h[i].states()))/z;
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cout << p << endl;
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}
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cout << endl;
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}
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cout << endl;
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// Reconstruct
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for (i=0; i < vts.getSize(); i++)
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{
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vts.getAt(i).probsUpdateLogistic(h[i], m_w, m_lambda, m_sigma);
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}
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printf("A fantasy... (v^, t>)\n");
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for (j=0; j < vts.getSize(); j++)
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{
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cout << vts.getAt(j).probs() << endl;
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}
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delete [] h;
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}
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VectorXd toHidden(const VectorXd& visible)
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{
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HiddenLayer th(m_w.getNumHidden());
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VisibleLayer tv(m_w.getNumVisible(), (const VectorXd*)&visible);
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th.probsUpdateLogistic(tv, m_w, m_lambda, m_sigma);
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return th.probs();
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}
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VectorXd toVisible(const VectorXd& hidden)
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{
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HiddenLayer th(m_w.getNumHidden(), (const VectorXd*)&hidden);
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VisibleLayer tv(m_w.getNumVisible());
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if (m_useVisibleGaussian)
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{
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tv.probsUpdateGaussian(th, m_w, m_lambda, m_sigma);
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}
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else
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{
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tv.probsUpdateLogistic(th, m_w, m_lambda, m_sigma);
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}
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return tv.probs();
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}
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VectorXd expectHidden(VectorXd visible, uint32_t numIter)
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{
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uint32_t i;
|
|
VisibleLayer v(m_w.getNumVisible(), (const VectorXd*)&visible);
|
|
HiddenLayer h(m_w.getNumHidden());
|
|
|
|
for (i=0; i < numIter; i++)
|
|
{
|
|
h.probsUpdateLogistic(v, (Weights&)m_w, m_lambda, m_sigma);
|
|
if (m_useVisibleGaussian)
|
|
{
|
|
v.probsUpdateGaussian(h, (Weights&)m_w, m_lambda, m_sigma);
|
|
}
|
|
else
|
|
{
|
|
v.probsUpdateLogistic(h, (Weights&)m_w, m_lambda, m_sigma);
|
|
}
|
|
}
|
|
|
|
return h.probs();
|
|
}
|
|
|
|
VectorXd expectVisible(VectorXd visible, uint32_t numIter)
|
|
{
|
|
uint32_t i;
|
|
VisibleLayer v(m_w.getNumVisible(), (const VectorXd*)&visible);
|
|
HiddenLayer h(m_w.getNumHidden());
|
|
|
|
for (i=0; i < numIter; i++)
|
|
{
|
|
h.probsUpdateLogistic(v, (Weights&)m_w, m_lambda, m_sigma);
|
|
if (m_useVisibleGaussian)
|
|
{
|
|
v.probsUpdateGaussian(h, (Weights&)m_w, m_lambda, m_sigma);
|
|
}
|
|
else
|
|
{
|
|
v.probsUpdateLogistic(h, (Weights&)m_w, m_lambda, m_sigma);
|
|
}
|
|
}
|
|
|
|
return v.probs();
|
|
}
|
|
|
|
void setSigma(double value)
|
|
{
|
|
m_sigma = value;
|
|
}
|
|
|
|
void setSigmaDecay(double value)
|
|
{
|
|
m_sigmaDecay = value;
|
|
}
|
|
|
|
void setWeightDecay(double value)
|
|
{
|
|
m_weightDecay = value;
|
|
}
|
|
|
|
void setLambda(double value)
|
|
{
|
|
m_lambda = value;
|
|
}
|
|
|
|
void setSparsity(double value)
|
|
{
|
|
m_sparsity = value;
|
|
}
|
|
|
|
void setUseVisibleGaussian(bool flag)
|
|
{
|
|
m_useVisibleGaussian = flag;
|
|
}
|
|
|
|
void setDoRaoBlackwell(bool flag)
|
|
{
|
|
m_doRaoBlackwell = flag;
|
|
}
|
|
|
|
void setUseProbsForHiddenReconstruction(bool flag)
|
|
{
|
|
m_useProbsForHiddenReconstruction = flag;
|
|
}
|
|
|
|
void setDoSparse(bool flag)
|
|
{
|
|
m_doSparse = flag;
|
|
}
|
|
|
|
void setNormalizeData(bool flag)
|
|
{
|
|
m_doNormalizeData = flag;
|
|
}
|
|
|
|
void setNumGibbs(uint32_t value)
|
|
{
|
|
m_numGibbs = value;
|
|
}
|
|
|
|
void setMuWeights(double value)
|
|
{
|
|
m_muWeights = value;
|
|
}
|
|
|
|
void setMuSparsity(double value)
|
|
{
|
|
m_muSparsity = value;
|
|
}
|
|
|
|
void setMomentum(double value)
|
|
{
|
|
m_momentum = value;
|
|
}
|
|
|
|
void cancel()
|
|
{
|
|
m_doCancel = true;
|
|
// while(m_doCancel);
|
|
}
|
|
|
|
private:
|
|
Weights &m_w;
|
|
RbmListener *m_pListener;
|
|
noise_gen_t m_noise;
|
|
double m_progress;
|
|
double m_sigma;
|
|
double m_sigmaDecay;
|
|
double m_weightDecay;
|
|
double m_lambda;
|
|
double m_sparsity;
|
|
double m_muWeights;
|
|
double m_muSparsity;
|
|
double m_momentum;
|
|
bool m_useVisibleGaussian;
|
|
bool m_doRaoBlackwell;
|
|
bool m_useProbsForHiddenReconstruction;
|
|
bool m_doSparse;
|
|
bool m_doNormalizeData;
|
|
volatile bool m_doCancel;
|
|
uint32_t m_numGibbs;
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
#endif /* RBM_HPP_ */
|