diff --git a/src/rbm/rbm.py b/src/rbm/rbm.py
index 48b21fa..2d460ac 100644
--- a/src/rbm/rbm.py
+++ b/src/rbm/rbm.py
@@ -1,8 +1,10 @@
+import numpy as np
 from layer import Layer
 from stack import Stack, StackType, StackException
+from stack_factory import StackFactory
 from params import RbmParams
 
-if __name__ == "__main__":
+def test():
 	params = RbmParams()
 	stack = Stack(StackType.Deep, "Stack")
 	dims = [(2, 3), (3, 4), (4, 5)]
@@ -21,4 +23,36 @@ if __name__ == "__main__":
 	stack.remove(lay1)
 	stack.remove(lay0)
 
+def read_armadillo(filename: str) -> np.ndarray:
+	result = None
+	with open(filename) as fp:
+		identifier = fp.readline().replace("\n", '')
+		if "ARMA_MAT_TXT_FN008" not in identifier:
+			raise Exception("Not a armadillo data file!")
+
+		line = fp.readline().replace("\n", '').split(' ')
+		shape = [int(s) for s in line]
+		print(f"shape: {shape}")
+
+		result = np.zeros(shape=shape, dtype=np.float64)
+		line = fp.readline().replace("\n", '').split('   ')
+		line = line[1:]
+		data = [float(s) for s in line]
+		for row in range(shape[0]):
+			result[row, :] = data
+
+	return result
+
+def main():
+	# Create stack from project file
+	stack = StackFactory.from_file("/home/jens/work/repos/Rbm/test.prj")
+
+	# Load train data
+	batch = read_armadillo("/home/jens/work/repos/Rbm/test.training.dat")
+
+	# Train
+	stack.train(batch)
+
+if __name__ == "__main__":
+	main()
 	print("Test: [passed]")
diff --git a/src/rbm/stack_deep.py b/src/rbm/stack_deep.py
index f4c51c5..8cbec59 100644
--- a/src/rbm/stack_deep.py
+++ b/src/rbm/stack_deep.py
@@ -1,5 +1,25 @@
+import numpy as np
+from status import Status
+from cd_train import cd_jens
 from stack import Stack, StackType
 
 class StackDeep(Stack):
 	def __init__(self, name: str):
 		Stack.__init__(self, StackType.Deep, name)
+
+	def batch_from(self, batch: np.ndarray, from_layer_id: int = 0):
+		_batch = np.matrix.copy(batch)
+		for index, layer in enumerate(self.layers):
+			if index == from_layer_id:
+				break
+			_batch = layer.v_to_ph(_batch)
+
+		return _batch
+
+	def train(self, batch: np.ndarray):
+		_batch = np.matrix.copy(batch)
+		for index, layer in enumerate(self.layers):
+			print(f"Train layer {index}")
+			_batch = self.batch_from(batch, index)
+			layer.train(_batch, cd_func=cd_jens, status=Status())
+
diff --git a/src/rbm/stack_factory.py b/src/rbm/stack_factory.py
index 97d1ea9..2ae7275 100644
--- a/src/rbm/stack_factory.py
+++ b/src/rbm/stack_factory.py
@@ -10,7 +10,7 @@ from stack_rnn import StackRnn
 
 class StackFactory:
 	@classmethod
-	def from_dict(cls, project: dict, layer_constructor: Callable = None) -> Stack:
+	def from_dict(cls, project: dict, layer_constructor: Callable = None) -> StackDeep|StackRnn:
 		name = project["stack"]["name"]
 		layers = project["stack"]["layers"]
 
@@ -56,7 +56,7 @@ class StackFactory:
 		return obj
 
 	@classmethod
-	def from_file(cls, filename: str, layer_constructor: Callable = None) -> Stack:
+	def from_file(cls, filename: str, layer_constructor: Callable = None) -> StackDeep|StackRnn:
 		obj = None
 		with open(filename, "r") as fp:
 			prj = json.load(fp)