refactored

src/tests/test_label.py

@@ -5,12 +5,14 @@ from rbm.label import Label
 
 if __name__ == "__main__":
 	voc_size = 100
-	do_train = False
+	do_train = True
 	work_dir = "../../results"
 	prj_root = "/home/jens/work/repos/Rbm"
 
 	# Create Labeler
-	enc = Label(voc_size, 16, Label.EncodingType.OneHot, work_dir)
+	label_w = 5
+	label_h = 5
+	enc = Label(voc_size, label_w*label_h, Label.EncodingType.Binary, work_dir)
 
 	# Load
 	enc.fitter.load()
@@ -34,7 +36,7 @@ if __name__ == "__main__":
 
 	fig, axes = plt.subplots(1, len(encoded), figsize=(12, 3))
 	for index, inp in enumerate(encoded):
-		img = np.reshape(inp, (4, 4))
+		img = np.reshape(inp, (label_w, label_h))
 		axes[index].imshow(img)
 		axes[index].axis('off')
 		axes[index].set_title(f'{test_labels[index]}')

src/tests/test_learn_encoded_labels.py

@@ -7,9 +7,9 @@ from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.matrix import Mat, np
 
 class LabelLearner(Model):
-	def __init__(self, name: str, work_dir: str = '.'):
+	def __init__(self, name: str, dim, work_dir: str = '.'):
 		super().__init__(name, work_dir)
-		self.unit1 = Entity((16, 8), EntityParams(), TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=10000))
+		self.unit1 = Entity(dim, EntityParams(do_gaussian_hidden=False), TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=10000))
 
 	def forward(self, x: Mat):
 		x = self.unit1.forward(x)
@@ -21,21 +21,30 @@ class LabelLearner(Model):
 
 if __name__ == "__main__":
 	voc_size = 100
+	do_train = False
 	work_dir = "../../results"
 	prj_root = "/home/jens/work/repos/Rbm"
 
 	# Create Labeler
-	enc = Label(voc_size, 16, work_dir)
+	label_w = 5
+	label_h = 5
+	enc = Label(voc_size, label_w*label_h, Label.EncodingType.OneHot, work_dir)
 
 	# Load
 	enc.fitter.load()
 
+	# Train
+	if do_train:
+		train_labels = list(range(voc_size))
+		enc.fit(train_labels)
+		enc.fitter.save()
+
 	# Encode test labels
 	test_labels = np.array([1, 23, 99, 37, 55, 7, 31, 10, 19, 70])
 	encoded, _ = enc.encode(test_labels)
 
 	# Create model
-	model = LabelLearner("Label-Learner", work_dir)
+	model = LabelLearner("Label-Learner", (label_w*label_h, 16), work_dir)
 
 	# Init state
 	model.init(0.01)
@@ -52,7 +61,7 @@ if __name__ == "__main__":
 	# Plot
 	fig, axes = plt.subplots(1, len(encoded), figsize=(12, 3))
 	for index, inp in enumerate(encoded):
-		img = np.reshape(inp, (4, 4))
+		img = np.reshape(inp, (label_w, label_h))
 		axes[index].imshow(img)
 		axes[index].axis('off')
 		axes[index].set_title(f'{test_labels[index]}')