improved tests

src/tests/test_model.py

@@ -1,22 +1,24 @@
 from rbm.model import Model
-from rbm.entity import Entity, EntityParams
+from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.matrix import Mat, np
-from rbm.train import TrainingParams
 
 class TestModel(Model):
 	def __init__(self, name: str, work_dir: str = '.'):
 		super().__init__(name, work_dir)
-		self.unit1 = Entity((16, 64), EntityParams())
-		self.unit2 = Entity((64, 16), EntityParams())
-		self.unit3 = Entity((16, 64), EntityParams())
+		self.unit1 = Entity((1024, 333), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+		self.unit2 = Entity((333, 64), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+		self.unit3 = Entity((64, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+		self.unit4 = Entity((128, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
 
 	def forward(self, x: Mat):
 		x = self.unit1.forward(x)
 		x = self.unit2.forward(x)
 		x = self.unit3.forward(x)
+		x = self.unit4.forward(x)
 		return x
 
 	def backward(self, x: Mat):
+		x = self.unit4.reconstruct(x)
 		x = self.unit3.reconstruct(x)
 		x = self.unit2.reconstruct(x)
 		x = self.unit1.reconstruct(x)
@@ -27,16 +29,16 @@ if __name__ == "__main__":
 	model = TestModel("TestModel", "results")
 
 	# Init state
-	model.init(0.01)
+	model.init(0.1)
 
 	# load state
 	model.load()
 
 	# create batch
-	batch = (np.random.rand(64, 16) > 0.5).astype(np.float64)
+	batch = (np.random.rand(64, 1024) > 0.5).astype(np.float64)
 
 	# Train
-	model.train(batch, TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=1000, num_gibbs_samples=3))
+	model.train(batch)
 
 	# save state
 	model.save()