- improved RnnStack

- AStack: context doesn't belong o training data

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@827 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/AStack.cpp

@@ -177,21 +177,6 @@ size_t AStack::loadTrainingBatch(const std::string &dir, bool doNormalize)
 		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;
 }
 

source/RnnStack.cpp

@@ -36,7 +36,7 @@ size_t RnnStack::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);
+	return v_to_vc(v, c);
 }
 
 arma::mat RnnStack::v_to_vc(const arma::mat& v, const arma::mat& c) const
@@ -63,16 +63,18 @@ arma::mat RnnStack::vc_to_v(const arma::mat& vc) const
 
 arma::mat RnnStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
 {
-	arma::mat vc = batch;
 	Layer *pLayer = getLayer(0);
+	arma::mat c = arma::zeros(batch.n_rows, m_numContext);
+	arma::mat v = vc_to_v(batch);
+	arma::mat vc = v_to_vc(v, c);
 	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);
+		c = pLayer->to_h_gibbs(vc);
+		v = arma::shift(vc_to_v(vc), layerId, 1);
 		vc = v_to_vc(v, c);
 		pLayer = pLayer->next;
 	}
@@ -81,24 +83,49 @@ arma::mat RnnStack::trainingBatchFrom(size_t layerId, const arma::mat& batch)
 
 void RnnStack::train(const arma::mat& batch, Rbm::IListener* pListener)
 {
-	arma::mat thisBatch = batch;
-	Layer *pLayer = getLayer(0);
-	while (pLayer)
+	// thisBatch = {padding | batch}
+	int numTraining = batch.n_rows;
+	arma::mat padding = arma::zeros(getSeqLen()-1, batch.n_cols);
+	arma::mat batch_padded = arma::join_cols(padding, batch);
+	arma::mat c = arma::zeros(numTraining, m_numContext);
+	
+	for (int i=0; i < getSeqLen(); i++)
 	{
-		thisBatch = trainingBatchFrom(pLayer->id(), thisBatch);
-		pLayer->train(thisBatch, pListener);
+		Layer *pLayer = getLayer(i);
+		int k = getSeqLen()-i-1;
+		arma::mat batch_shifted = batch_padded.rows(k, numTraining+k-1);
+		arma::mat vc = v_to_vc(batch_shifted, c);
+		pLayer->train(vc, pListener);
+		c = pLayer->to_h_gibbs(vc);
 		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);
-	}
 }
 
+arma::mat RnnStack::step_forward(arma::mat &state, const arma::mat& v_curr)
+{
+	arma::mat c = arma::zeros(1, m_numContext);
+	arma::mat z = arma::zeros(arma::size(v_curr));
+	arma::mat r;
+	
+	if (state.is_empty())
+	{
+		state = arma::zeros(getSeqLen(), v_curr.n_cols);
+	}
+	state = arma::shift(state, 1, 0);
+	state.row(0) = v_curr;
+	
+	for (int j=0; j < getSeqLen(); j++)
+	{
+		Layer *pLayer = getLayer(j);
+		arma::mat v = arma::join_rows(state.row(j), z);
+		arma::mat vc = v_to_vc(v, c);
+		c = pLayer->to_h_gibbs(vc);
+		vc = pLayer->to_v_gibbs(c);
+		r = to_next(vc_to_v(vc));
+	}
+	return r;
+}

source/RnnStack.hpp

@@ -18,6 +18,8 @@
 
 class RnnStack : public AStack
 {
+	const size_t NUM_CODES = 37;
+	
 public:
 	RnnStack(const std::string &name, size_t numContext);
 	RnnStack(const RnnStack& orig) = delete;
@@ -34,9 +36,20 @@ public:
     arma::mat vc_to_v(const arma::mat &vc) const;
     arma::mat vc_to_c(const arma::mat &vc) const;
 	
+	arma::mat step_forward(arma::mat &state, const arma::mat &v);
+
+	arma::mat to_next(const arma::mat &v)
+	{
+		return v.cols(0, NUM_CODES-1);
+	}
+
+	arma::mat to_curr(const arma::mat &v)
+	{
+		return v.cols(NUM_CODES, 2*NUM_CODES-1);
+	}
+	
 private:
 	size_t m_numContext;
-	
 };
 
 #endif /* RNNSTACK_HPP */

source/poet.cpp

@@ -35,7 +35,7 @@ class RbmListener : public Rbm::IListener
 
 const char punctuation[] = {' ', '.', '!', '?', 0};
 const int NUM_CODES = 1 + 26 + 10;
-const int SEQ_LENGTH = 5;
+const int SEQ_LENGTH = 2;
 
 int char_is(char c, const char *pTable)
 {
@@ -192,12 +192,14 @@ arma::mat to_next(arma::mat v)
 }
 
 #define CREATE_TRAINING		0
-#define DO_TRAINING			1
+#define DO_TRAINING			0
+#define DO_FORWARD			1
+
 int main()
 {
 #if CREATE_TRAINING	
 	arma::mat batch = createTraining("moby_ch1.txt", SEQ_LENGTH);
-	batch.save("poet.training.dat", arma::arma_ascii);
+	batch.save("poet2.training.dat", arma::arma_ascii);
 	return 0;
 #endif
 	
@@ -210,19 +212,53 @@ int main()
 	// Load training
 	stack->loadTrainingBatch(".");
 
+	arma::mat t_vc = stack->trainingBatch();
+
 #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->getLayer(i)->weightsInit(0.1,0);
 	}
+	stack->train(t_vc, &listener);
 	stack->saveWeights(".");
 	stack->save(".");		
 
+	printf("Curr\n");
+	for (int i=0; i < t_vc.n_rows; i++)
+	{
+		arma::mat curr = stack->to_curr(t_vc.row(i));
+		char c = idx2ch(curr.index_max());
+		putchar(c);
+	}
+	printf("\n");
+	printf("Next\n");
+	for (int i=0; i < t_vc.n_rows; i++)
+	{
+		arma::mat next = stack->to_next(t_vc.row(i));
+		char c = idx2ch(next.index_max());
+		putchar(c);
+	}
+	printf("\n");
+
+#endif
+	
+#if DO_FORWARD
+	arma::mat state;
+	
+	for (int i=0; i < t_vc.n_rows; i++)
+	{
+		arma::mat curr = stack->to_curr(t_vc.row(i));
+		arma::mat next = stack->to_next(t_vc.row(i));
+		arma::mat r = stack->step_forward(state, curr);
+		char c = idx2ch(r.index_max());
+		putchar(c);
+	}
+	printf("\n");
+
 #else
+	return 0;
 	Layer *layer = stack->getLayer(0);
 	int numTraining = stack->trainingBatch().n_rows;