- training params are (again) attrubute of Entity

- ditched layout concept

src/rbm/train.py

@@ -1,50 +1,9 @@
-from collections.abc import Callable
 from .matrix import sample, sample_gaussian, prob, rms_error_accu, Mat, np
 from .entity import Entity
 from .status import Status
 
-class TrainingParams:
-	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 = 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):
-		obj = TrainingParams()
-		obj.learning_rate = params["learningRate"]
-		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"]
-
-		return obj
-
-def cd_jens(entity: Entity, v_states: Mat, params: TrainingParams):
+def cd_jens(entity: Entity, v_states: Mat):
+	params = entity.training_params
 	v_probs = prob(v_states)
 	h_states = entity.forward(v_states)
 	h_probs = h_states
@@ -83,7 +42,8 @@ def cd_jens(entity: Entity, v_states: Mat, params: TrainingParams):
 
 	return dw, dbv, dbh
 
-def cd_binary_binary(entity: Entity, data_pos: Mat, params: TrainingParams):
+def cd_binary_binary(entity: Entity, data_pos: Mat):
+	params = entity.training_params
 	# Positive phase
 	h_probs_pos = prob(entity.h_given_v(data_pos))
 
@@ -112,7 +72,7 @@ def cd_binary_binary(entity: Entity, data_pos: Mat, params: TrainingParams):
 
 	return dw, dbv, dbh
 
-def cd_gaussian_binary(entity: Entity, data_pos: Mat, params: TrainingParams):
+def cd_gaussian_binary(entity: Entity, data_pos: Mat):
 	# Positive phase
 	h_probs_pos = entity.h_given_v(data_pos)
 
@@ -135,7 +95,7 @@ def cd_gaussian_binary(entity: Entity, data_pos: Mat, params: TrainingParams):
 
 	return dw, dbv, dbh
 
-def cd_gaussian_gaussian(entity: Entity, data_pos: Mat, params: TrainingParams):
+def cd_gaussian_gaussian(entity: Entity, data_pos: Mat):
 	# Positive phase
 	h_probs_pos = entity.h_given_v(data_pos)
 
@@ -170,7 +130,11 @@ def to_mini_batch(batch: Mat, mini_batch_size: int):
 
 	return mini_batches
 
-def train(entity: Entity, batch: Mat, params: TrainingParams, status: Status):
+def train(entity: Entity, batch: Mat, status: Status):
+	params = entity.training_params
+	if params is None:
+		return False
+
 	mini_batch_size = min(params.mini_batch_size, batch.shape[0]) if params.mini_batch_size > 0 else batch.shape[0]
 	d_progress = 100.0 / (batch.shape[0]*params.num_epochs)
 	progress = 0
@@ -191,7 +155,7 @@ def train(entity: Entity, batch: Mat, params: TrainingParams, status: Status):
 		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)
 
 			# 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)
@@ -213,4 +177,4 @@ def train(entity: Entity, batch: Mat, params: TrainingParams, status: Status):
 	status.on_change({"progress": {"value": round(progress), "unit": "%"},
 					  "err_rms_total": {"value": err_rms, "unit": ""}})
 
-	return None
+	return True