[RBM]

- DBN fixes
- batch sample inside training loop
- implemented RbmComponent stacking

git-svn-id: http://moon:8086/svn/software/trunk/projects/RBM@296 b431acfa-c32f-4a4a-93f1-934dc6c82436

Source/Rbm.hpp

@@ -32,7 +32,7 @@ public:
 	{
 	}
 
-	virtual void onEpochTrained(const Rbm &obj) = 0;
+	virtual void onProgressChanged(const Rbm &obj) = 0;
 };
 
 class Rbm
@@ -274,8 +274,6 @@ public:
 
 		MatrixXd diffErr(batchSize, m_w.getNumVisible());
 
-		m_progress = 0;
-
 		MatrixXd batch = m_batch;
 		
 		if (m_params.m_doNormalizeData)
@@ -288,14 +286,18 @@ public:
 			}
 		}
 		
-		if (!m_params.m_useProbsForHiddenReconstruction)
-		{
-			sample(batch);
-		}
-		
+		m_progress = 0;
 		for (epoch=0; epoch < numEpochs; epoch++)
 		{
-			double err;
+			if (m_pListener)
+			{
+				m_pListener->onProgressChanged(*this);
+			}
+
+			if (!m_params.m_useProbsForHiddenReconstruction)
+			{
+				sample(batch, m_batch);
+			}
 
 			// Create hidden layer base on training data
 			toHiddenBatch(h, batch);
@@ -311,7 +313,6 @@ public:
 				sumBiasH = h.colwise().sum();
 			}
 			sumWeights = batch.transpose() * h;
-			diffErr = batch;
 
 			for (gibbs=0; gibbs < m_params.m_numGibbs; gibbs++)
 			{
@@ -350,7 +351,6 @@ public:
 				sumBiasH -= h.colwise().sum();
 			}
 			sumWeights -= m_v.transpose() * h;
-			diffErr -= m_v;
 
 			deltaWeights = m_params.m_momentum*deltaWeights + m_params.m_muWeights*(kTrain*sumWeights - m_params.m_weightDecay*m_w.weights());
 			m_w.weights() += deltaWeights;
@@ -385,11 +385,12 @@ public:
 			m_progress += dProgress;
 			if (m_pListener)
 			{
-				m_pListener->onEpochTrained(*this);
+				m_pListener->onProgressChanged(*this);
 			}
 
+			diffErr = batch - m_v;
 			diffErr.array() *= diffErr.array();
-			err = diffErr.colwise().sum().sum();
+			double err = diffErr.colwise().sum().sum();
 
 			cout << "err =" << endl;
 			cout << err << endl;
@@ -445,11 +446,6 @@ public:
 		 m_variableSigma.fill(m_params.m_constantSigma);
 	}
 
-	double getConstantSigma()
-	{
-		 return m_params.m_constantSigma;
-	}
-
 	RowVectorXd& getVariableSigma()
 	{
 		 return m_variableSigma;