- refactored

source/Rbm.cpp

@@ -20,7 +20,7 @@ using namespace Matutils;
 Rbm::Rbm(size_t numVisible, size_t numHidden)
 : m_params()
 , m_whv(numVisible, numHidden)
-, m_bhv(1, numHidden)
+, m_bh(1, numHidden)
 , m_bv(1, numVisible)
 {
 	assert(numVisible > 0);
@@ -30,7 +30,7 @@ Rbm::Rbm(size_t numVisible, size_t numHidden)
 Rbm::Rbm(const Rbm& orig)
 : m_params(orig.m_params)
 , m_whv(orig.m_whv)
-, m_bhv(orig.m_bhv)
+, m_bh(orig.m_bh)
 , m_bv(orig.m_bv)
 {
 }
@@ -41,7 +41,7 @@ Rbm::~Rbm()
 
 size_t Rbm::numHidden() const
 {
-	return m_bhv.size();
+	return m_bh.size();
 }
 
 size_t Rbm::numVisible() const
@@ -79,14 +79,14 @@ arma::mat Rbm::toVisibleProbs(const arma::mat& hidden) const
 void Rbm::weightsAssign(const arma::mat& w, const arma::mat& bhv, const arma::mat& bv)
 {
 	m_whv.submat(0, 0, w.n_rows - 1, w.n_cols - 1) = w;
-	m_bhv.submat(0, 0, bhv.n_rows - 1, bhv.n_cols - 1) = bhv;
+	m_bh.submat(0, 0, bhv.n_rows - 1, bhv.n_cols - 1) = bhv;
 	m_bv.submat(0, 0, bv.n_rows - 1, bv.n_cols - 1) = bv;
 }
 
 void Rbm::weightsInit(double stddev, double mu)
 {
 	uniform(m_whv, stddev, mu);
-	uniform(m_bhv, stddev, mu);
+	uniform(m_bh, stddev, mu);
 	uniform(m_bv, stddev, mu);
 }
 
@@ -128,7 +128,7 @@ void Rbm::gibbs_hv(arma::mat &h_probs, arma::mat &v_probs) const
 	}
 }
 
-void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::mat &dbhv, arma::mat &dbv)
+void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma::mat &dbv)
 {
 	arma::mat v_probs(v_states);
 	arma::mat h_states = v_to_h(v_states);
@@ -154,7 +154,7 @@ void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::
 	// Update weights (positive phase)
 	dw = v_states.t() * h_states;
 	dbv = sum(v_states, 0);
-	dbhv = sum(h_states, 0);
+	dbh = sum(h_states, 0);
 
 	// Gibbs sampling with training params
 	for (int i=0; i < m_params.numGibbs; i++)
@@ -187,7 +187,7 @@ void Rbm::contrastiveDivergence(arma::mat const &v_states, arma::mat &dw, arma::
 	// Update weights (negative phase)
 	dw -= v_probs.t() * h_probs;
 	dbv -= sum(v_probs, 0);
-	dbhv -= sum(h_probs, 0);
+	dbh -= sum(h_probs, 0);
 }
 
 void Rbm::train(arma::mat const &batch, IListener* pListener)
@@ -199,11 +199,11 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 	int batchRowIndex = 0;
 	
 	arma::mat grad_bias_v(arma::zeros(1, m_bv.n_cols));
-	arma::mat grad_bias_hv(arma::zeros(1, m_bhv.n_cols));
+	arma::mat grad_bias_hv(arma::zeros(1, m_bh.n_cols));
 	arma::mat grad_weight_hv(arma::zeros(m_whv.n_rows, m_whv.n_cols));
 	arma::mat momentum_whv = arma::zeros(m_whv.n_rows, m_whv.n_cols);
 	arma::mat momentum_bias_v(arma::zeros(1, m_bv.n_cols));
-	arma::mat momentum_bias_hv(arma::zeros(1, m_bhv.n_cols));
+	arma::mat momentum_bias_hv(arma::zeros(1, m_bh.n_cols));
 	arma::mat penalty_weights = arma::zeros(m_whv.n_rows, m_whv.n_cols);
 
 	int trainingSizeRemain = batch.n_rows;
@@ -243,7 +243,7 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 			momentum_whv = m_params.momentum*momentum_whv + grad_weight_hv - status.L2*penalty_weights;
 			
 			m_bv += learning_rate*momentum_bias_v;
-			m_bhv += learning_rate*momentum_bias_hv;
+			m_bh += learning_rate*momentum_bias_hv;
 			m_whv += learning_rate*momentum_whv;
 			
 			progress += dProgress*miniBatchSizeActual;
@@ -286,7 +286,7 @@ arma::mat Rbm::prob(const arma::mat &src)
 
 arma::mat Rbm::v_to_h(const arma::mat &visible) const
 {
-	return visible * m_whv  + arma::repmat(m_bhv, visible.n_rows, 1);
+	return visible * m_whv  + arma::repmat(m_bh, visible.n_rows, 1);
 }
 
 arma::mat Rbm::h_to_v(const arma::mat &hidden) const
@@ -306,7 +306,7 @@ const arma::mat& Rbm::bv() const
 
 const arma::mat& Rbm::bh() const
 {
-	return m_bhv;
+	return m_bh;
 }
 
 

source/Rbm.hpp

@@ -157,7 +157,7 @@ public:
 
 protected:
 	Params m_params;
-	arma::mat m_bhv;
+	arma::mat m_bh;
 	arma::mat m_bv;
 
 private: