- state load/save revised

- num gibbs samples is also part of entity
- working 3-layer deep test model

src/tests/test_model.py

@@ -5,36 +5,46 @@ from rbm.entity import Entity, EntityParams
 from rbm.matrix import Mat, np
 from rbm.train import TrainingParams
 
-class DeepStack(Model):
-	def __init__(self, name: str = "myStack"):
-		super().__init__(name)
-		self.unit1 = Entity((32*32*3, 256), EntityParams(do_gaussian_visible=True))
-#		self.unit2 = Entity((16, 24), EntityParams())
-#		self.unit3 = Entity((24, 10), EntityParams())
+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())
 
 	def forward(self, x: Mat):
-		x = self.unit1(x)
-#		x = self.unit2(x)
-#		x = self.unit3(x)
+		x = self.unit1.forward(x)
+		x = self.unit2.forward(x)
+		x = self.unit3.forward(x)
 		return x
 
+	def backward(self, x: Mat):
+		x = self.unit3.reconstruct(x)
+		x = self.unit2.reconstruct(x)
+		x = self.unit1.reconstruct(x)
+		return x
 
 if __name__ == "__main__":
 	# Create model
-	model = DeepStack("myStack")
+	model = TestModel("TestModel", "results")
+
+	# Init state
+	model.init(0.01)
 
 	# load state
 	model.load()
 
 	# create batch
-	batch = (np.random.rand(16, 32*32*3) > 0.5).astype(np.float64)
+	batch = (np.random.rand(64, 16) > 0.5).astype(np.float64)
 
 	# Train
-	model.train(batch, TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+	model.train(batch, TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=1000, num_gibbs_samples=3))
 
 	# save state
 	model.save()
 
-	for pat in batch:
-		model.forward(pat)
+	for inp in batch:
+		out = model.backward(model.forward(inp))
+		print(f"Pattern: {inp} -> {out}")
+