refactored

src/rbm/layer.py

@@ -10,19 +10,14 @@ class Layer:
 		self.name = name
 		self.shape = shape
 		self.entity = Entity((shape[0]*shape[1]+shape[2], shape[3]), params)
-		self.state_filename = f"{self.name}_state.npz"
 
 	def init(self, std: float):
 		self.entity.state.init(mu=0, std=std)
 
 	def save(self, filename: str = None):
-		if filename is None:
-			filename = self.state_filename
 		self.entity.state.to_file(filename)
 
 	def load(self, filename: str = None):
-		if filename is None:
-			filename = self.state_filename
 		state = RbmState.from_file(filename)
 		if state is not None:
 			self.entity.state = state

src/rbm/status.py

@@ -29,4 +29,5 @@ class Status:
 		return False
 
 	def on_report(self, status: dict) -> bool:
+		Status.print_status(status)
 		return True

src/tests/test_rbm.py

@@ -1,10 +1,10 @@
 import os.path
 import cv2 as cv
 from argparse import ArgumentParser
-from .stack_factory import StackFactory
-from .status import Status
-from .stack_deep import StackDeep
-from .matrix import Mat, np, convert
+from rbm.stack_factory import StackFactory
+from rbm.status import Status
+from rbm.stack_deep import StackDeep
+from rbm.matrix import Mat, np, convert
 
 def cv_show(name: str, vec: Mat, shape):
 	img = cv.Mat(convert(np.resize(vec, shape)))

src/tests/test_xor.py

@@ -1,9 +1,12 @@
+import os.path
+
 from rbm.params import EntityParams
 from rbm.layer import Layer
 from rbm.status import Status
 from rbm.train import train
 from rbm.matrix import Mat, np
 
+work_dir = "../../results"
 def xor():
 	# Create params
 	params = EntityParams()
@@ -17,7 +20,7 @@ def xor():
 	layer.init(0.01)
 
 	# Load weights (if exists)
-	layer.load()
+	layer.load(os.path.join(work_dir, "xor_layer0_state.npz"))
 
 	# Prepare training data
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
@@ -26,7 +29,7 @@ def xor():
 	train(layer.entity, training_batch, Status())
 
 	# Save weights
-	layer.save()
+	layer.save(os.path.join(work_dir, "xor_layer0_state.npz"))
 
 	# Test with test data
 	test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)