- moved context awareness from Rbm to Layer (final)

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@770 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/Rbm.cpp

@@ -16,20 +16,14 @@
 
 #define RBM_TRAIN_FLAT 0
 
-Rbm::Rbm(size_t numVisible, size_t numHidden, size_t numContext)
+Rbm::Rbm(size_t numVisible, size_t numHidden)
 : m_params()
-, m_whv(numVisible+numContext, numHidden)
+, m_whv(numVisible, numHidden)
 , m_bhv(1, numHidden)
-, m_bv(1, numVisible+numContext)
-, m_ctx()
+, m_bv(1, numVisible)
 {
 	assert(numVisible > 0);
 	assert(numHidden > 0);
-	if (numContext)
-	{
-		assert(numContext == numHidden);
-		m_ctx.resize(1, numContext);
-	}
 	Noise_Init(&m_noise, 0x32727155);
 }
 
@@ -38,7 +32,6 @@ Rbm::Rbm(const Rbm& orig)
 , m_whv(orig.m_whv)
 , m_bhv(orig.m_bhv)
 , m_bv(orig.m_bv)
-, m_ctx(orig.m_ctx)
 {
 }
 
@@ -52,7 +45,6 @@ void Rbm::weightsInit(double stddev, double mu)
 	uniform(m_whv, stddev, mu);
 	uniform(m_bhv, stddev, mu);
 	uniform(m_bv, stddev, mu);
-	uniform(m_ctx, stddev, mu);
 }
 
 void Rbm::fromJson(Json::Value rbm)
@@ -95,7 +87,7 @@ void Rbm::gibbs(arma::mat &hv_probs, arma::mat &v_probs)
 	}
 }
 
-void Rbm::weightUpdate(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 &dbhv, arma::mat &dbv)
 {
 	arma::mat v_probs(v_states);
 	arma::mat h_states = v_to_h(v_states);
@@ -139,7 +131,6 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 	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 penalty_weights = arma::zeros(m_whv.n_rows, m_whv.n_cols);
-	arma::mat ctx = arma::zeros(1, numContext());
 
 	int trainingSizeRemain = batch.n_rows;
 
@@ -165,8 +156,10 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 		
 		for (int epoch=0; epoch < m_params.numEpochs; epoch++)
 		{
-			weightUpdate(v_states, grad_weight_hv, grad_bias_hv, grad_bias_v);
+			// Contrastive divergence learning: calculate gradients
+			contrastiveDivergence(v_states, grad_weight_hv, grad_bias_hv, grad_bias_v);
 			
+			// Adjust weight and biases
 			penalty_weights = weight_decay*arma::sign(m_whv);
 			
 			status.L1 = accu(abs(m_whv));
@@ -182,6 +175,7 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 			progress += dProgress*miniBatchSizeActual;
 			status.progress = (int)(progress + 0.5);
 
+			// Update status
 			if (status.progress != lastProgress)
 			{
 				lastProgress = status.progress;
@@ -204,6 +198,7 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 			
 	} // number of mini batches
 
+	// Update final status
 	arma::mat diffErr = batch - prob(h_to_v(prob(v_to_h(batch))));
 	arma::mat diffErr_squared = diffErr % diffErr;
 	status.err_total = accu(diffErr_squared)/diffErr_squared.n_elem;
@@ -234,25 +229,6 @@ arma::mat Rbm::sample(const arma::mat &src)
 	return dst;
 }
 
-arma::mat Rbm::vc_to_v(const arma::mat &vc) const
-{
-	return arma::reshape(vc, 1, numVisible() - numContext());
-}
-
-arma::mat Rbm::vc_to_c(const arma::mat &vc) const
-{
-	if (numContext() == 0)
-	{
-		return arma::mat(1,0);
-	}
-	return vc.submat(0, numVisible() - numContext(), 0, numVisible() - 1);
-}
-
-arma::mat Rbm::to_vc(const arma::mat &v, const arma::mat &c) const
-{
-	return arma::join_rows(v, c);
-}
-
 arma::mat Rbm::v_to_h(const arma::mat &visible) const
 {
 	return visible * m_whv  + arma::repmat(m_bhv, visible.n_rows, 1);