- refactored

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@825 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/AStack.cpp

@@ -157,3 +157,68 @@ arma::mat AStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
 	return thisBatch;
 }
 
+arma::mat& AStack::trainingBatch()
+{
+	return m_trainingBatch;
+}
+
+size_t AStack::loadTrainingBatch(const std::string &dir, bool doNormalize)
+{
+	std::string filename = dir + "/" + m_name + ".training.dat";
+	std::string path = dir + "/" + m_name + ".training.dat";
+	bool success = m_trainingBatch.load(filename, arma::arma_ascii);
+
+	if (success)
+	{
+		if (doNormalize)
+		{
+			m_trainingBatch = Rbm::normalize(m_trainingBatch);
+		}
+		std::cout << "Loaded " << m_trainingBatch.n_rows << " training samples\n";
+	}
+	
+	// Migrate context part to training data
+	size_t numTraining = m_trainingBatch.n_rows;
+	size_t numVisible = getLayer(0)->numVisible();
+	
+	if (m_trainingBatch.n_cols < numVisible)
+	{
+		size_t diff = numVisible - m_trainingBatch.n_cols;
+		arma::mat training_with_ctx = arma::join_rows(m_trainingBatch, arma::zeros(numTraining, diff));
+		m_trainingBatch = training_with_ctx;
+	}
+	else if (m_trainingBatch.n_cols > numVisible)
+	{
+		m_trainingBatch = m_trainingBatch.submat(0, 0, numTraining-1, numVisible-1);
+	}
+
+	return m_trainingBatch.n_rows;
+}
+
+size_t AStack::saveTrainingBatch(const std::string &dir)
+{
+	std::string filename = dir + "/" + m_name + ".training.dat";
+	bool success = m_trainingBatch.save(filename, arma::arma_ascii);
+
+	if (success)
+	{
+		std::cout << "Saved " << m_trainingBatch.n_rows << " training samples\n";
+	}
+	
+	return m_trainingBatch.n_rows;
+}
+
+size_t AStack::numTraining()
+{
+	return m_trainingBatch.n_rows;
+}
+
+void AStack::addTraining(const arma::mat &toAdd)
+{ 
+	m_trainingBatch.insert_rows(m_trainingBatch.n_rows, toAdd);
+}
+
+void AStack::delTraining(int index)
+{
+	m_trainingBatch.shed_row(index);
+}

source/AStack.hpp

@@ -68,13 +68,21 @@ public:
 	virtual void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) = 0;
 	virtual void train(const arma::mat& batch, Rbm::IListener* pListener) = 0;
 	arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch);	
-	
+
+	size_t numTraining();
+	void addTraining(const arma::mat &toAdd);
+	void delTraining(int index);
+	size_t loadTrainingBatch(const std::string &dir, bool doNormalize=false);
+	size_t saveTrainingBatch(const std::string &dir);
+	arma::mat& trainingBatch();
+
 protected:
 	StackType m_type;
 	std::string m_name;
 	Layer *m_pLayers;
 
 private:
+	arma::mat m_trainingBatch;
 	
 };
 

source/DeepStack.cpp

@@ -52,72 +52,6 @@ void DeepStack::train(size_t layerId, const arma::mat& batch, Rbm::IListener* pL
 	pLayer->train(thisBatch, pListener);
 }
 
-arma::mat& DeepStack::trainingBatch()
-{
-	return m_trainingBatch;
-}
-
-size_t DeepStack::loadTrainingBatch(const std::string &dir, bool doNormalize)
-{
-	std::string filename = dir + "/" + m_name + ".training.dat";
-	std::string path = dir + "/" + m_name + ".training.dat";
-	bool success = m_trainingBatch.load(filename, arma::arma_ascii);
-
-	if (success)
-	{
-		if (doNormalize)
-		{
-			m_trainingBatch = Rbm::normalize(m_trainingBatch);
-		}
-		std::cout << "Loaded " << m_trainingBatch.n_rows << " training samples\n";
-	}
-	
-	// Migrate context part to training data
-	size_t numTraining = m_trainingBatch.n_rows;
-	size_t numVisible = getLayer(0)->numVisible();
-	
-	if (m_trainingBatch.n_cols < numVisible)
-	{
-		size_t diff = numVisible - m_trainingBatch.n_cols;
-		arma::mat training_with_ctx = arma::join_rows(m_trainingBatch, arma::zeros(numTraining, diff));
-		m_trainingBatch = training_with_ctx;
-	}
-	else if (m_trainingBatch.n_cols > numVisible)
-	{
-		m_trainingBatch = m_trainingBatch.submat(0, 0, numTraining-1, numVisible-1);
-	}
-
-	return m_trainingBatch.n_rows;
-}
-
-size_t DeepStack::saveTrainingBatch(const std::string &dir)
-{
-	std::string filename = dir + "/" + m_name + ".training.dat";
-	bool success = m_trainingBatch.save(filename, arma::arma_ascii);
-
-	if (success)
-	{
-		std::cout << "Saved " << m_trainingBatch.n_rows << " training samples\n";
-	}
-	
-	return m_trainingBatch.n_rows;
-}
-
-size_t DeepStack::numTraining()
-{
-	return m_trainingBatch.n_rows;
-}
-
-void DeepStack::addTraining(const arma::mat &toAdd)
-{ 
-	m_trainingBatch.insert_rows(m_trainingBatch.n_rows, toAdd);
-}
-
-void DeepStack::delTraining(int index)
-{
-	m_trainingBatch.shed_row(index);
-}
-
 arma::mat DeepStack::upPass(size_t layerId, const arma::mat& v)
 {
 	arma::mat h = arma::zeros(0,0);

source/DeepStack.hpp

@@ -35,15 +35,6 @@ public:
     arma::mat downPass(size_t layerId, arma::mat const &h);
     arma::mat upDownPass(size_t layerId, arma::mat const &v);
 
-	size_t numTraining();
-	void addTraining(const arma::mat &toAdd);
-	void delTraining(int index);
-	size_t loadTrainingBatch(const std::string &dir, bool doNormalize=false);
-	size_t saveTrainingBatch(const std::string &dir);
-	arma::mat& trainingBatch();
-
-private:
-	arma::mat m_trainingBatch;
 };
 
 

source/poet.cpp

@@ -10,6 +10,7 @@
 #include "Rbm.hpp"
 #include "Layer.hpp"
 #include "DeepStack.hpp"
+#include "StackCreator.hpp"
 
 using namespace std;
 using namespace arma;
@@ -199,21 +200,19 @@ int main()
 	return 0;
 #endif
 	
-	DeepStack stack(".", "poet5");
-	
 	// Load project
-	stack.load();
+	AStack *stack = StackCreator::fromFile(".", "poet5");
 	
 	// Load weights
-	stack.loadWeights();
+	stack->loadWeights(".");
 	
 	// Load training
-	stack.loadTrainingBatch();
+	stack->loadTrainingBatch(".");
 
-	Layer *layer = stack.getLayer(0);
-	int numTraining = stack.trainingBatch().n_rows;
+	Layer *layer = stack->getLayer(0);
+	int numTraining = stack->trainingBatch().n_rows;
 	
-	arma::mat t = stack.trainingBatch();
+	arma::mat t = stack->trainingBatch();
 	
 	arma::mat h = layer->toHiddenProbs(t);
 	arma::mat r = layer->toVisibleProbs(h);