- removed Optimizer

- refactored training
- use static seed for random (for better comparison)
- simplified StackDeep.train

src/rbm/stack_deep.py

@@ -1,34 +1,18 @@
 from .status import Status
-from .train import train, Optimizer
+from .train import train
 from .stack import Stack, StackType
 from .matrix import Mat, np
 
-USE_OPTIMIZER = False
-
 class StackDeep(Stack):
 	def __init__(self, name: str, work_dir: str = '.'):
 		Stack.__init__(self, StackType.Deep, name, work_dir)
 
-	def batch_from(self, batch: Mat, from_layer_id: int = 0):
-		_batch = np.copy(batch)
-		for index, layer in enumerate(self.layers):
-			if index == from_layer_id:
-				break
-			_batch = layer.entity.forward(_batch)
-
-		return _batch
-
 	def train(self, batch: Mat, status=Status()):
 		_batch = np.copy(batch)
 		for index, layer in enumerate(self.layers):
 			print(f"Train layer {index} for {layer.training_params.num_epochs} epochs")
-			if USE_OPTIMIZER:
-				optim = Optimizer(layer.entity, layer.training_params)
-				_batch = optim(_batch, status=status)
-			else:
-				_batch = self.batch_from(batch, index)
-				train(layer.entity, _batch, layer.training_params, status=status)
-
+			train(layer.entity, _batch, layer.training_params, status=status)
+			_batch = layer.entity.forward(_batch)
 
 	def pass_up(self, visible: Mat, from_layer_id: int = 0):
 		h = np.copy(visible)