test_norbs: added mode hidden gaussian - hidden binary

src/tests/test_norbs.py

@@ -6,9 +6,16 @@ from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.matrix import Mat, np, read_armadillo
 
 class TestModel(Model):
-	def __init__(self, name: str, work_dir: str = '.'):
+	def __init__(self, name: str, work_dir: str = '.', do_gaussian_hidden=False):
 		super().__init__(name, work_dir)
-		self.unit1 = Entity((96*96, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.000001, momentum=0.9, num_epochs=1000))
+
+		if do_gaussian_hidden:
+			# Hidden gaussian
+			self.unit1 = Entity((96*96, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True), TrainingParams(learning_rate=0.00001, momentum=0.9, num_epochs=1000, do_rao_blackwell=True))
+		else:
+			# Hidden binary
+			self.unit1 = Entity((96 * 96, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
+								TrainingParams(learning_rate=0.000002, momentum=0.9, num_epochs=1000))
 
 	def forward(self, x: Mat):
 		x = self.unit1.forward(x)
@@ -24,20 +31,19 @@ if __name__ == "__main__":
 	prj_root = "/home/jens/work/repos/Rbm"
 
 	# Create model
-	model = TestModel("norb_small_16h_v2", "results")
+	model = TestModel(prj_name, "results")
 
 	# Init state
 	model.init(0.01)
 
 	# load state
-	model.load()
+#	model.load()
 
 	# Load train data
 	train_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.training.dat"))
-	test_batch = train_batch
 
 	# Load test data
-	#test_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.test.dat"))
+	test_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.test.dat"))
 
 	# Train
 	model.train(train_batch)