- 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/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;