added model

src/rbm/entity.py

@@ -2,10 +2,10 @@ from .state import RbmState
 from .matrix import prob, Mat
 
 class EntityParams:
-	def __init__(self):
+	def __init__(self, do_gaussian_visible: bool = False, do_gaussian_hidden: bool = False):
 		# Entity parameters
-		self.do_gaussian_visible = False
-		self.do_gaussian_hidden = False
+		self.do_gaussian_visible = do_gaussian_visible
+		self.do_gaussian_hidden = do_gaussian_hidden
 
 	@classmethod
 	def from_dict(cls, params: dict):
@@ -24,6 +24,9 @@ class Entity:
 		self.state = RbmState.from_layer_params(shape)
 		self.grad = RbmState.from_layer_params(shape)
 
+	def __call__(self, x: Mat):
+		return self.forward(x)
+
 	def grad_zero(self):
 		self.grad = RbmState.from_layer_params(self.shape)
 

src/rbm/model.py

@@ -0,0 +1,45 @@
+import os
+from abc import ABC, abstractmethod
+
+from .entity import Entity
+from .train import train, TrainingParams, cd_jens
+from .matrix import Mat, np
+from .status import Status
+from .state import RbmState
+
+class Model(ABC):
+	known_classes = [Entity]
+	def __init__(self, name: str = "myStack", work_dir: str = "."):
+		self.name = name
+		self.work_dir = work_dir
+
+	def objects(self, obj_type: type = Entity) -> list[Entity]:
+		obj_list: list[type[obj_type]] = []
+		for name, value in self.__dict__.items():
+			if isinstance(value, obj_type):
+				obj_list.append(value)
+		return obj_list
+
+	def train(self, batch: Mat, params: TrainingParams):
+		_batch = np.copy(batch)
+		for entity in self.objects(Entity):
+			train(entity, _batch, params, Status(), cd_jens)
+			_batch = entity(_batch)
+
+	@abstractmethod
+	def forward(self, x: Mat) -> Mat:
+		pass
+
+	def save(self):
+		for index, entity in enumerate(self.objects(Entity)):
+			filepath = os.path.join(self.work_dir, f"{self.name}-{index}-state.npz")
+			entity.state.to_file(filepath)
+
+	def load(self):
+		for index, entity in enumerate(self.objects(Entity)):
+			filepath = os.path.join(self.work_dir, f"{self.name}-{index}-state.npz")
+			state = RbmState.from_file(filepath)
+			if state is not None:
+				entity.state = state
+
+

src/rbm/train.py

@@ -4,18 +4,29 @@ from .entity import Entity
 from .status import Status
 
 class TrainingParams:
-	def __init__(self):
+	def __init__(self,
+				 learning_rate: float = 0.1,
+				 momentum: float = 0.5,
+				 weight_decay: float = 0.0,
+				 num_epochs: int = 1000,
+				 num_gibbs_samples: int = 1,
+				 mini_batch_size: int = 0,
+				 do_rao_blackwell: bool = False,
+				 do_gibbs_sample_visible: bool = False,
+				 do_gibbs_sample_hidden: bool = False,
+				 do_batch_sample: bool = False
+				 ):
 		# Training parameters
-		self.learning_rate = 0.1
-		self.momentum = 0.5
-		self.weight_decay = 0
-		self.num_epochs = 1000
-		self.mini_batch_size = 0
-		self.do_rao_blackwell = False
-		self.do_gibbs_sample_visible = False
-		self.do_gibbs_sample_hidden = False
-		self.do_batch_sample = False
-		self.num_gibbs_samples = 1
+		self.learning_rate = learning_rate
+		self.momentum = momentum
+		self.weight_decay = weight_decay
+		self.num_epochs = num_epochs
+		self.num_gibbs_samples = num_gibbs_samples
+		self.mini_batch_size = mini_batch_size
+		self.do_rao_blackwell = do_rao_blackwell
+		self.do_gibbs_sample_visible = do_gibbs_sample_visible
+		self.do_gibbs_sample_hidden = do_gibbs_sample_hidden
+		self.do_batch_sample = do_batch_sample
 
 	@classmethod
 	def from_dict(cls, params: dict):
@@ -24,12 +35,12 @@ class TrainingParams:
 		obj.momentum = params["momentum"]
 		obj.weight_decay = params["weightDecay"]
 		obj.num_epochs = params["numEpochs"]
+		obj.num_gibbs_samples = params["numGibbs"]
 		obj.mini_batch_size = params["miniBatchSize"]
 		obj.do_rao_blackwell = params["doRaoBlackwell"]
 		obj.do_gibbs_sample_visible = params["gibbsDoSampleVisible"]
 		obj.do_gibbs_sample_hidden = params["gibbsDoSampleHidden"]
 		obj.do_batch_sample = params["doSampleBatch"]
-		obj.num_gibbs_samples = params["numGibbs"]
 
 		return obj
 
@@ -70,7 +81,7 @@ def cd_jens(entity: Entity, v_states: Mat, params: TrainingParams):
 	dbv -= np.sum(v_probs, 0)
 	dbh -= np.sum(h_probs, 0)
 
-	return dw, dbv, dbh, h_probs
+	return dw, dbv, dbh
 
 def to_mini_batch(batch: Mat, mini_batch_size: int):
 	mini_batches = []
@@ -97,7 +108,7 @@ def train(entity: Entity, batch: Mat, params: TrainingParams, status: Status, cd
 		entity.grad_zero()
 		for epochs in range(params.num_epochs):
 			# Contrastive divergence learning: calculate gradients
-			dwhv, dbv, dbh, _ = cd_func(entity, mini_batch, params)
+			dwhv, dbv, dbh = cd_func(entity, mini_batch, params)
 
 			# Adjust weight and biases
 			grad = entity.grad_compute(dbv, dbh, dwhv, learning_rate=params.learning_rate/batch.shape[0], momentum=params.momentum, weight_decay=params.weight_decay)

src/tests/test_model.py

@@ -0,0 +1,40 @@
+from sympy.codegen.ast import float64
+
+from rbm.model import Model
+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())
+
+	def forward(self, x: Mat):
+		x = self.unit1(x)
+#		x = self.unit2(x)
+#		x = self.unit3(x)
+		return x
+
+
+if __name__ == "__main__":
+	# Create model
+	model = DeepStack("myStack")
+
+	# load state
+	model.load()
+
+	# create batch
+	batch = (np.random.rand(16, 32*32*3) > 0.5).astype(np.float64)
+
+	# Train
+	model.train(batch, TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+
+	# save state
+	model.save()
+
+	for pat in batch:
+		model.forward(pat)
+