diff --git a/JayRnn.py b/JayRnn.py
index bc119be..20723ed 100644
--- a/JayRnn.py
+++ b/JayRnn.py
@@ -24,11 +24,11 @@ from rbm.train import train
 from rbm.status import Status
 
 # ── Hyper-parameters ──────────────────────────────────────────────────────────
-TEXT    = "  JAY IS COOL"   # the character sequence to learn (matches diagram example)
+TEXT    = "  HALLO SUPER JENS. SUPER!    " # the character sequence to learn (matches diagram example)
 WIN    = 3         # sliding-window width  (= N in the diagram)
 STRIDE = 1     	   # sliding-window step size
 UNITS  = 1
-H_SIZE = 64         # hidden units per RBM cell
+H_SIZE = 120         # hidden units per RBM cell
 
 TRAIN_PARAMS = TrainingParams(
 	learning_rate    = 0.05,
@@ -71,7 +71,7 @@ class RnnModel(Model):
 
 		self.units: list[Entity] = []
 		for _ in range(UNITS):
-			unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000), enable_training=True)
+			unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), training_params=TRAIN_PARAMS)
 			self.units.append(unit)
 
 	def train(self, vc: Mat, status: Status = None):
@@ -91,17 +91,21 @@ class RnnModel(Model):
 
 
 if __name__ == "__main__":
-	vec = idx2vec(ch2idx("JENS"))
-	vec = clamp(vec, axis=1)
-	indices = vec2idx(vec, axis=1)
-	ch_str = idx2ch(indices)
-	print(ch_str)
+	if 1:
+		# Test of helper function
+		# ToDo: move to tests
+		vec = idx2vec(ch2idx("JENS"))
+		vec = clamp(vec, axis=1)
+		indices = vec2idx(vec, axis=1)
+		ch_str = idx2ch(indices)
+		print(ch_str)
 
-	win_text = to_window(TEXT)
-	print(win_text)
+		win_text = to_window(TEXT)
+		print(win_text)
+
+		batch = to_batch(win_text)
+		print(batch)
 
-	batch = to_batch(win_text)
-	print(batch)
 
 	model = RnnModel(name='JayRnn', work_dir='results')
 	model.init(0.01)
@@ -111,14 +115,14 @@ if __name__ == "__main__":
 	# context will be updated after training
 	c_train = np.zeros([len(batch), H_SIZE])
 	vc_train = concat(batch, c_train, axis=1)
-#	model.train(vc_train, status=Status())
+	model.train(vc_train, status=Status())
 	model.save()
 
 	# test the model
-	seed = str2vec('JA^')
+	seed = str2vec('HA^')
 	v_forward = seed.flatten()
 	context = c_train[0]
-	for i in range(len(TEXT)):
+	for i in range(len(TEXT)+5):
 		print(f"Forward {i:02d}: {vec2str(v_forward.reshape([WIN, vocab_size()]), axis=1)}")
 		vc_forward = concat(v_forward.reshape([1, WIN*vocab_size()]), context.reshape([1, H_SIZE]), axis=1)
 		vc_predict = model.forward_step(vc_forward)