- removed Optimizer

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

src/tests/test_xor.py

@@ -2,7 +2,7 @@ import os.path
 
 from rbm.layer import Layer
 from rbm.status import Status
-from rbm.train import train, TrainingParams, Optimizer
+from rbm.train import train, TrainingParams
 from rbm.matrix import Mat, np
 from rbm.entity import EntityParams
 
@@ -29,11 +29,7 @@ def xor():
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
-	if USE_OPTIMIZER:
-		optim = Optimizer(layer.entity, training_params)
-		optim(training_batch, Status())
-	else:
-		train(layer.entity, training_batch, training_params, Status())
+	train(layer.entity, training_batch, training_params, Status())
 
 	# Save weights
 	layer.save(os.path.join(WORK_DIR, "xor_layer0_state.npz"))