- integrated RnnStack

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@826 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/AStack.hpp

@@ -54,6 +54,10 @@ public:
 	StackType type();
 	void setName(const std::string &name);
 	size_t numLayers();
+	virtual size_t numContext()
+	{
+		return 0;
+	}
 	void addLayer(Layer *pLayer);
 	void delLayer(Layer *pLayer);
 	
@@ -67,7 +71,7 @@ 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);	
+	virtual arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch) = 0;	
 
 	size_t numTraining();
 	void addTraining(const arma::mat &toAdd);

source/DeepStack.cpp

@@ -24,6 +24,22 @@ DeepStack::~DeepStack()
 { 
 }
 
+arma::mat DeepStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
+{
+	arma::mat thisBatch = batch;
+	Layer *pLayer = getLayer(0);
+	while (pLayer)
+	{
+		if (pLayer->id() == layerId)
+		{
+			break;
+		}
+		thisBatch = pLayer->toHiddenProbs(thisBatch);
+		pLayer = pLayer->next;
+	}
+	return thisBatch;
+}
+
 void DeepStack::train(const arma::mat& batch, Rbm::IListener* pListener)
 {
 	arma::mat thisBatch = batch;

source/DeepStack.hpp

@@ -28,6 +28,7 @@ public:
 	DeepStack(const DeepStack& orig) = delete;
 	virtual ~DeepStack();
 	
+	arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch);
 	void train(const arma::mat& batch, Rbm::IListener* pListener) override;
 	void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) override;
 	

source/Layer.cpp

@@ -118,23 +118,6 @@ arma::mat Layer::vc_to_v(const arma::mat& vc) const
 	return arma::reshape(vc, 1, numVisible() - m_numContext);
 }
 
-void Layer::calcContextBatch(arma::mat& batch)
-{
-	size_t numTraining = batch.n_rows;
-
-	if (m_numContext > 0 and numTraining > 0)
-	{
-		for (int i = 1; i < numTraining; i++)
-		{
-			arma::mat v = batch.row(i - 1);
-			arma::mat h = arma::zeros(1, numHidden());
-			gibbs_vh(v, h);
-			arma::mat training_with_ctx = arma::join_rows(batch.row(i).cols(0, numVisible() - m_numContext - 1), h);
-			batch.row(i) = training_with_ctx;
-		}
-	}
-}
-
 void Layer::train(const arma::mat& batch, IListener* pListener)
 {
 	std::cout << m_name << ": " << " Training of layer " << std::to_string(id()) << std::endl;

source/Layer.hpp

@@ -48,7 +48,6 @@ public:
     bool weightsLoad(std::string const &dir, std::string const &prj);
     bool weightsSave(std::string const &dir, std::string const &prj);
 
-    void calcContextBatch(arma::mat &batch);
     void train(arma::mat const &batch, IListener *pListener=nullptr);
 	
 	arma::mat to_h_gibbs(const arma::mat& v_probs);

source/MainComponent.cpp

@@ -903,8 +903,7 @@ const juce::String& MainComponent::getBaseDir()
 void MainComponent::run()
 {
     trainButton->setButtonText (TRANS("Stop"));
-	m_pLayer->calcContextBatch(m_stack->trainingBatch());
+//	m_pLayer->calcContextBatch(m_stack->trainingBatch());
-//	m_pLayer->train(m_stack->trainingBatch(), this);
 	m_stack->train(m_pLayer->id(), m_stack->trainingBatch(), this);
     trainButton->setButtonText (TRANS("Train"));
 }

source/RnnStack.cpp

@@ -13,8 +13,9 @@
 
 #include "RnnStack.hpp"
 
-RnnStack::RnnStack(const std::string &name)
+RnnStack::RnnStack(const std::string &name, size_t numContext)
 : AStack(StackType::Rnn, name)
+, m_numContext(numContext)
 {
 }
 
@@ -22,13 +23,82 @@ RnnStack::~RnnStack()
 {
 }
 
+size_t RnnStack::getSeqLen()
+{
+	return numLayers();
+}
+
+size_t RnnStack::numContext()
+{
+	return m_numContext;
+}
+
+arma::mat RnnStack::v_to_vc(const arma::mat& v) const
+{
+	arma::mat c = arma::zeros(v.n_rows, m_numContext);
+	return arma::join_rows(v, c);
+}
+
+arma::mat RnnStack::v_to_vc(const arma::mat& v, const arma::mat& c) const
+{
+	return arma::join_rows(v, c);
+}
+
+arma::mat RnnStack::vc_to_c(const arma::mat& vc) const
+{
+	size_t numVisible = vc.n_cols;
+	
+	if (m_numContext == 0)
+	{
+		return arma::mat(1, 0);
+	}
+	return vc.submat(0, numVisible - m_numContext, 0, numVisible - 1);
+}
+
+arma::mat RnnStack::vc_to_v(const arma::mat& vc) const
+{
+	size_t numVisible = vc.n_cols;
+	return vc.submat(0, 0, vc.n_rows - 1, numVisible - m_numContext - 1);
+}
+
+arma::mat RnnStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
+{
+	arma::mat vc = batch;
+	Layer *pLayer = getLayer(0);
+	while (pLayer)
+	{
+		if (layerId == pLayer->id())
+		{
+			break;
+		}
+		arma::mat c = pLayer->to_h_gibbs(vc);
+		arma::mat v = arma::shift(vc_to_v(vc), layerId, 1);
+		vc = v_to_vc(v, c);
+		pLayer = pLayer->next;
+	}
+	return vc;
+}
 
 void RnnStack::train(const arma::mat& batch, Rbm::IListener* pListener)
 {
-
+	arma::mat thisBatch = batch;
+	Layer *pLayer = getLayer(0);
+	while (pLayer)
+	{
+		thisBatch = trainingBatchFrom(pLayer->id(), thisBatch);
+		pLayer->train(thisBatch, pListener);
+		pLayer = pLayer->next;
+	}
 }
 
 void RnnStack::train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener)
 {
-
+	arma::mat thisBatch = batch;
+	Layer *pLayer = getLayer(layerId);
+	if (pLayer)
+	{
+		thisBatch = trainingBatchFrom(pLayer->id(), batch);
+		pLayer->train(thisBatch, pListener);
+	}
 }
+

source/RnnStack.hpp

@@ -19,15 +19,23 @@
 class RnnStack : public AStack
 {
 public:
-	RnnStack(const std::string &name);
+	RnnStack(const std::string &name, size_t numContext);
 	RnnStack(const RnnStack& orig) = delete;
 	virtual ~RnnStack();
 
+	arma::mat trainingBatchFrom(size_t layerId, const arma::mat& batch) override;
 	void train(const arma::mat& batch, Rbm::IListener* pListener) override;
 	void train(size_t layerId, const arma::mat& batch, Rbm::IListener* pListener) override;
 	
+	size_t getSeqLen();
+	size_t numContext();
+	arma::mat v_to_vc(const arma::mat &v) const;
+	arma::mat v_to_vc(const arma::mat &v, const arma::mat &c) const;
+    arma::mat vc_to_v(const arma::mat &vc) const;
+    arma::mat vc_to_c(const arma::mat &vc) const;
 	
 private:
+	size_t m_numContext;
 	
 };
 

source/StackCreator.cpp

@@ -34,11 +34,12 @@ AStack* StackCreator::fromJson(Json::Value& project, LayerConstructor *pLayerCon
 	Json::Value &stack = project["stack"];
 	Json::Value &layers = project["stack"]["layers"];
 	int type = stack["type"].asInt();
+	int numContext = stack["numContext"].asInt();
 	
 	AStack *pStack = nullptr;
 	if (type == AStack::StackType::Rnn)
 	{
-		pStack = new RnnStack(name);
+		pStack = new RnnStack(name, numContext);
 	}
 	else
 	{
@@ -94,6 +95,7 @@ bool StackCreator::toFile(AStack* pStack, const std::string& dir, const std::str
 	project["stack"]["name"] = name;
 	project["stack"]["type_string"] = AStack::stackTypeStrings[pStack->type()];
 	project["stack"]["type"] = pStack->type();
+	project["stack"]["numContext"] = pStack->numContext();
 	
 	Json::Value layers(Json::arrayValue);
 	Layer *pLayer = pStack->getLayer(0);

source/poet.cpp

@@ -9,7 +9,7 @@
 #include <jsoncpp/json/json.h>
 #include "Rbm.hpp"
 #include "Layer.hpp"
-#include "DeepStack.hpp"
+#include "RnnStack.hpp"
 #include "StackCreator.hpp"
 
 using namespace std;
@@ -140,11 +140,11 @@ arma::mat createTraining(const string &filename, size_t seq_len)
 struct Rnn
 {
 	Rnn(Layer *layer)
-	: nV(layer->numVisible() - layer->context().n_cols)
+	: nV(layer->numVisible() - layer->numHidden())
 	, nH(layer->numHidden())
 	, nVx(layer->numVisibleX())
 	, nVy(layer->numVisibleY())
-	, nC(layer->context().n_cols)
+	, nC(layer->numHidden())
 	{
 		
 	}
@@ -192,6 +192,7 @@ arma::mat to_next(arma::mat v)
 }
 
 #define CREATE_TRAINING		0
+#define DO_TRAINING			1
 int main()
 {
 #if CREATE_TRAINING	
@@ -201,7 +202,7 @@ int main()
 #endif
 	
 	// Load project
-	AStack *stack = StackCreator::fromFile(".", "poet5");
+	RnnStack *stack = reinterpret_cast<RnnStack*>(StackCreator::fromFile(".", "poet2"));
 	
 	// Load weights
 	stack->loadWeights(".");
@@ -209,6 +210,19 @@ int main()
 	// Load training
 	stack->loadTrainingBatch(".");
 
+#if DO_TRAINING
+	RbmListener listener;
+	arma::mat t_vc = stack->trainingBatch();
+	
+	for (int i=0; i < stack->getSeqLen(); i++)
+	{
+		stack->getLayer(i)->weightsInit(0.1,0);
+		stack->train(i, t_vc, &listener);
+	}
+	stack->saveWeights(".");
+	stack->save(".");		
+
+#else
 	Layer *layer = stack->getLayer(0);
 	int numTraining = stack->trainingBatch().n_rows;
 	
@@ -268,7 +282,7 @@ int main()
 		putchar(c);
 	}
 
+#endif
 	printf("\n\nEnd of program\n");
-	
 	return 0;
 }