418 lines
9.1 KiB
C++
418 lines
9.1 KiB
C++
/*
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* To change this license header, choose License Headers in Project Properties.
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* To change this template file, choose Tools | Templates
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* and open the template in the editor.
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*/
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/*
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* File: Rbm.cpp
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* Author: jens
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*
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* Created on 21. Oktober 2019, 21:28
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*/
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#include <cassert>
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#include "Rbm.hpp"
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#include "matutils.hpp"
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using namespace Matutils;
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#define USE_CD_HINTON 1
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Rbm::Rbm(size_t numVisible, size_t numHidden)
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: m_params()
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, m_whv(numVisible, numHidden)
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, m_bh(1, numHidden)
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, m_bv(1, numVisible)
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{
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assert(numVisible > 0);
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assert(numHidden > 0);
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}
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Rbm::Rbm(const Rbm& orig)
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: m_params(orig.m_params)
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, m_whv(orig.m_whv)
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, m_bh(orig.m_bh)
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, m_bv(orig.m_bv)
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{
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}
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Rbm::~Rbm()
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{
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}
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size_t Rbm::numHidden() const
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{
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return m_bh.size();
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}
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size_t Rbm::numVisible() const
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{
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return m_bv.size();
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}
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Rbm::Params& Rbm::params()
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{
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return m_params;
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}
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arma::mat Rbm::rms_error(arma::mat diffErr)
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{
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arma::mat diffErr_squared = diffErr % diffErr;
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return arma::sum(diffErr_squared, 1) * 1.0 / diffErr_squared.n_cols;
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}
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double Rbm::rms_error_accu(arma::mat diffErr)
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{
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arma::mat diffErr_squared = diffErr % diffErr;
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return arma::accu(diffErr_squared) / diffErr_squared.n_elem;
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}
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arma::mat Rbm::toHiddenProbs(const arma::mat& visible) const
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{
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arma::mat state = v_to_h(visible);
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if (m_params.doGaussianHidden)
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{
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return state;
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}
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return prob(state);
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}
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arma::mat Rbm::toVisibleProbs(const arma::mat& hidden) const
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{
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arma::mat state = h_to_v(hidden);
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if (m_params.doGaussianVisible)
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{
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return state;
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}
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return prob(state);
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}
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void Rbm::weightsAssign(const arma::mat& w, const arma::mat& bh, const arma::mat& bv)
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{
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m_whv.submat(0, 0, w.n_rows - 1, w.n_cols - 1) = w;
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m_bh.submat(0, 0, bh.n_rows - 1, bh.n_cols - 1) = bh;
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m_bv.submat(0, 0, bv.n_rows - 1, bv.n_cols - 1) = bv;
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}
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void Rbm::weightsInit(double stddev, double mu)
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{
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m_whv = uniform(m_whv, stddev, mu);
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m_bh = uniform(m_bh, 0, 0);
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m_bv = uniform(m_bv, 0, 0);
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}
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void Rbm::fromJson(Json::Value rbm)
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{
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std::cout << "Importing Rbm" << std::endl;
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m_params.fromJson(rbm["params"]);
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}
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Json::Value Rbm::toJson() const
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{
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std::cout << "Exporting Rbm" << std::endl;
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Json::Value rbm;
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rbm["params"] = m_params.toJson();
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return rbm;
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}
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void Rbm::gibbs_vh(arma::mat &v_probs, arma::mat &h_probs) const
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{
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for (int i=0; i < m_params.numGibbs; i++)
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{
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// Create hidden representation given v
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h_probs = toHiddenProbs(v_probs);
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// Create visible reconstruction (a fantasy...) given hid
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v_probs = toVisibleProbs(h_probs);
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}
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}
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void Rbm::gibbs_hv(arma::mat &h_probs, arma::mat &v_probs) const
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{
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for (int i=0; i < m_params.numGibbs; i++)
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{
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// Create visible reconstruction (a fantasy...) given hid
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v_probs = toVisibleProbs(h_probs);
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// Create hidden representation given v
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h_probs = toHiddenProbs(v_probs);
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}
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}
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void Rbm::cd(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
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{
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#if USE_CD_HINTON
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{
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cd_hinton(v_data, dw, dbh, dbv);
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}
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#else
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{
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cd_jens(v_data, dw, dbh, dbv);
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}
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#endif
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}
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void Rbm::cd_hinton_hid_linear(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
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{
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// Start positive phase
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arma::mat poshidprobs = v_to_h(v_data);
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arma::mat posprods = v_data.t() * poshidprobs;
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arma::mat poshidact = arma::sum(poshidprobs);
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arma::mat posvisact = arma::sum(v_data);
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// End of positive phase
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arma::mat poshidstates = poshidprobs + arma::randn(arma::size(poshidprobs));
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// Start negative phase
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arma::mat negdata = prob(h_to_v(poshidstates));
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arma::mat neghidprobs = v_to_h(negdata);
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arma::mat negprods = negdata.t() * neghidprobs;
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arma::mat neghidact = arma::sum(neghidprobs);
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arma::mat negvisact = arma::sum(negdata);
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// Update weight deltas
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dw = posprods - negprods;
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dbv = posvisact - negvisact;
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dbh = poshidact - neghidact;
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}
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void Rbm::cd_hinton(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
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{
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// Start positive phase
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arma::mat poshidprobs = toHiddenProbs(v_data);
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arma::mat posprods;
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arma::mat poshidact = arma::sum(poshidprobs);
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arma::mat posvisact = arma::sum(v_data);
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// End of positive phase
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arma::mat poshidstates;
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if (m_params.doGaussianHidden)
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{
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if (m_params.gibbsDoSampleHidden)
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{
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poshidstates = poshidprobs + arma::randn(arma::size(poshidprobs));
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}
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else
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{
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poshidstates = poshidprobs;
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}
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}
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else
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{
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poshidstates = sample(poshidprobs);
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}
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if (m_params.doRaoBlackwell)
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{
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posprods = v_data.t() * poshidprobs;
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}
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else
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{
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posprods = v_data.t() * sample(poshidprobs);
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}
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arma::mat negprods;
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arma::mat neghidact;
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arma::mat negvisact;
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for (int i=0; i < m_params.numGibbs; i++)
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{
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// Start negative phase
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arma::mat negdata;
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if (m_params.gibbsDoSampleHidden)
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{
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negdata = toVisibleProbs(sample(poshidstates));
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}
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else
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{
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negdata = toVisibleProbs(poshidstates);
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}
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arma::mat neghidprobs;
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if (m_params.gibbsDoSampleVisible)
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{
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neghidprobs = toHiddenProbs(sample(negdata));
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}
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else
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{
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neghidprobs = toHiddenProbs(negdata);
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}
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negprods = negdata.t() * neghidprobs;
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neghidact = arma::sum(neghidprobs);
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negvisact = arma::sum(negdata);
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poshidprobs = neghidprobs;
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}
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// Update weight deltas
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dw = posprods - negprods;
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dbv = posvisact - negvisact;
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dbh = poshidact - neghidact;
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}
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void Rbm::cd_jens(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
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{
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arma::mat v_probs(v_states);
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arma::mat h_states = toHiddenProbs(v_states);
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arma::mat h_probs;
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// Sample hidden
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if (m_params.doGaussianHidden)
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{
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h_probs = h_states;
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h_states = h_probs + arma::randn(h_probs.n_rows, h_probs.n_cols);
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}
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else if (m_params.doRaoBlackwell)
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{
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h_probs = toHiddenProbs(v_states);
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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_probs = toHiddenProbs(v_states);
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h_states = sample(h_probs);
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}
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// Update weights (positive phase)
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dw = v_states.t() * h_states;
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dbv = sum(v_states, 0);
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dbh = sum(h_states, 0);
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// Gibbs sampling with training params
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for (int i=0; i < m_params.numGibbs; i++)
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{
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// Create visible reconstruction (a fantasy...) given hid
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if (m_params.gibbsDoSampleHidden)
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{
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v_probs = toVisibleProbs(sample(h_probs));
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}
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else
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{
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v_probs = toVisibleProbs(h_probs);
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}
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// Create hidden representation given v
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if (m_params.gibbsDoSampleVisible)
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{
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h_probs = toHiddenProbs(sample(v_probs));
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}
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else
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{
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h_probs = toHiddenProbs(v_probs);
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}
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}
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// Update weights (negative phase)
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dw -= v_probs.t() * h_probs;
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dbv -= sum(v_probs, 0);
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dbh -= sum(h_probs, 0);
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}
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void Rbm::train(arma::mat const &batch, IListener* pListener)
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{
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Status status;
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double dProgress = 100.0/(batch.n_rows*m_params.numEpochs);
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double progress = 0;
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int lastProgress = -100;
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int batchRowIndex = 0;
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arma::mat dbv(arma::zeros(1, m_bv.n_cols));
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arma::mat dbh(arma::zeros(1, m_bh.n_cols));
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arma::mat dwhv(arma::zeros(m_whv.n_rows, m_whv.n_cols));
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arma::mat inc_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
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arma::mat inc_bv(arma::zeros(1, m_bv.n_cols));
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arma::mat inc_bh(arma::zeros(1, m_bh.n_cols));
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int trainingSizeRemain = batch.n_rows;
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bool shouldAbort = false;
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while (trainingSizeRemain && !shouldAbort)
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{
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int miniBatchSizeActual = std::min(m_params.miniBatchSize, trainingSizeRemain);
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arma::mat miniBatch = batch.rows(batchRowIndex, batchRowIndex+miniBatchSizeActual-1);
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trainingSizeRemain -= miniBatchSizeActual;
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batchRowIndex += miniBatchSizeActual;
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int numcases = std::min(m_params.miniBatchSize, (int)batch.n_rows);
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arma::mat v_states(miniBatch);
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// Create hidden layer base on training data
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if (m_params.doSampleBatch)
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{
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// When the hidden units are being driven by data, always use stochastic binary states
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v_states = sample(miniBatch);
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}
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for (int epoch=0; epoch < m_params.numEpochs; epoch++)
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{
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// Contrastive divergence learning: calculate gradients
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cd(v_states, dwhv, dbh, dbv);
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// Adjust weight and biases
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inc_bv = m_params.momentum*inc_bv + m_params.learningRate/numcases*dbv;
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inc_bh = m_params.momentum*inc_bh + m_params.learningRate/numcases*dbh;
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inc_whv = m_params.momentum*inc_whv + m_params.learningRate*(dwhv/numcases - m_params.weightDecay*m_whv);
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m_bv += inc_bv;
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m_bh += inc_bh;
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m_whv += inc_whv;
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progress += dProgress*miniBatchSizeActual;
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status.progress = (int)(progress + 0.5);
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// Update status
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if (status.progress != lastProgress)
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{
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lastProgress = status.progress;
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// Calculate error
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status.err = rms_error_accu(miniBatch - toVisibleProbs(toHiddenProbs(v_states)));
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if (pListener)
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{
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if(!pListener->onProgress(this, status))
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{
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shouldAbort = true;
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break;
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}
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}
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}
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} // Number of epochs
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} // number of mini batches
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// Update final status
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status.err_total = rms_error_accu(batch - toVisibleProbs(toHiddenProbs(batch)));
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if (pListener)
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{
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pListener->onProgress(this, status);
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}
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}
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arma::mat Rbm::v_to_h(const arma::mat &visible) const
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{
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return visible * m_whv + arma::repmat(m_bh, visible.n_rows, 1);
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}
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arma::mat Rbm::h_to_v(const arma::mat &hidden) const
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{
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return hidden * m_whv.t() + arma::repmat(m_bv, hidden.n_rows, 1);
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}
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const arma::mat& Rbm::whv() const
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{
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return m_whv;
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}
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const arma::mat& Rbm::bv() const
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{
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return m_bv;
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}
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const arma::mat& Rbm::bh() const
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{
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return m_bh;
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}
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