diff --git a/JayRnn.py b/JayRnn.py
index 4ecc3b5..b85309e 100644
--- a/JayRnn.py
+++ b/JayRnn.py
@@ -24,16 +24,16 @@ from rbm.train import train
 from rbm.status import Status
 
 # ── Hyper-parameters ──────────────────────────────────────────────────────────
-TEXT    = "0123456789" # the character sequence to learn (matches diagram example)
+TEXT    = "HALLO MAUSI! SUPER HASE!" # the character sequence to learn (matches diagram example)
 WIN    = 3         # sliding-window width  (= N in the diagram)
 STRIDE = 1     	   # sliding-window step size
 UNITS  = 3
-H_SIZE = 32         # hidden units per RBM cell
+H_SIZE = 64         # hidden units per RBM cell
 NUM_EPOCHS = 1000
 NUM_ITERATIONS = 1
 
 TRAIN_PARAMS = TrainingParams(
-	learning_rate    = 0.04,
+	learning_rate    = 0.1,
 	momentum         = 0.5,
 	num_epochs       = NUM_EPOCHS,
 	do_rao_blackwell = True,
@@ -76,6 +76,9 @@ def batch_delay(_batch: Mat, delay=0):
 
 def vc2char(_vc: Mat):
 	_v, _ = split(_vc.reshape(1, WIN*vocab_size()+H_SIZE), H_SIZE, axis=1)
+	return v2char(_v)
+
+def v2char(_v: Mat):
 	return vec2str(_v.reshape([WIN, vocab_size()]), axis=1)
 
 class RnnModel(Model):
@@ -101,13 +104,14 @@ class RnnModel(Model):
 
 	def forward_step(self, _vc: Mat):
 		for unit in self.units:
+#			print(f"forward_step in : {unit.name}:{vc2char(_vc)}")
 			_c = unit.forward(_vc)
 			_vc = unit.reconstruct(_c)
+			print(f"forward_step out: {unit.name}:{vc2char(_vc)}")
 			_v, _ = split(_vc, H_SIZE, axis=1)
-			_v_cl = clamp(_v.reshape([WIN, vocab_size()]), axis=1).reshape([1, WIN * vocab_size()])
-			_v = shift_left(_v_cl, 1)
+			_v_cl = clamp(_v.reshape([WIN, vocab_size()]), axis=1)
+			_v = shift_left(_v_cl.reshape([1, WIN*vocab_size()]), vocab_size())
 			_vc = concat(_v, _c, axis=1)
-			print(f"forward_step: {unit.name}:{vc2char(_vc)}")
 		return _vc
 
 	def forward(self, x: Mat):
@@ -139,27 +143,14 @@ if __name__ == "__main__":
 	# context will be updated after training
 	c_train = np.zeros([batch.shape[0], H_SIZE])
 	vc_train = concat(batch, c_train, axis=1)
-#	model.train(vc_train, status=Status())
-#	model.save()
+	model.train(vc_train, status=Status())
+	model.save()
 
 	# test the model
-	seed_str = '1'
-	seed_padded = '^'*(WIN-len(seed_str)) + seed_str
+	seed_str = 'HA'
+	seed_padded = seed_str + '^'*(WIN-len(seed_str))
 	v_test = str2vec(seed_padded).reshape([1, WIN*vocab_size()])
 	c_test = np.zeros([1, H_SIZE])
 	vc_test = concat(v_test, c_test, axis=1)
-	for i in range(len(TEXT) + 5):
+	for i in range(len(TEXT)):
 		vc_test = model.forward_step(vc_test)
-	if 0:
-		for i in range(len(TEXT)+5):
-			v_mat = v_forward.reshape([WIN, vocab_size()])
-			print(f"Forward {i:02d}: {vec2str(v_mat, axis=1)}")
-			v_predict, context = model.forward_step(v_mat, context)
-			v_predict_mat = v_predict.reshape([WIN, vocab_size()])
-			v_predict_clamp = clamp(v_predict_mat, axis=1)
-			v_predict_str = vec2str(v_predict_clamp, axis=1)
-			text_predict += v_predict_str[WIN-1]
-			print(f"Predict {i:02d}: {v_predict_str}")
-			v_forward = shift_left(v_predict_clamp.reshape([1, WIN*vocab_size()]), vocab_size())
-
-		print(seed_padded[0:WIN-1] + text_predict)
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