Optimizer.train() shall ignore param.do_batch_sample

2025-12-19 17:29:32 +01:00
parent d3c9fe4681
commit 5f5c7c6d77
1 changed files with 4 additions and 6 deletions
@@ -145,17 +145,13 @@ class Optimizer:
 		d_progress = 100.0 / self.params.num_epochs
 		progress = 0

-		v_states = batch
-		if self.params.do_batch_sample:
-			v_states = sample(batch)
-
-		forward = None
 		for epochs in range(self.params.num_epochs):
-			forward = self.epoch(v_states)
+			self.epoch(batch)

 			# check if status update is needed
 			if status.want_report(round(progress)):
 				# Calculate	error
+				forward = self.entity.forward(batch)
 				result = self.entity.reconstruct(forward)
 				err_rms = rms_error_accu(batch - result)
 				if not status.on_change({"progress": {"value": round(progress), "unit": "%"},
@@ -164,6 +160,7 @@ class Optimizer:

 			progress += d_progress

+		forward = self.entity.forward(batch)
 		err_rms = rms_error_accu(batch - self.entity.reconstruct(forward))
 		status.on_change({"progress": {"value": round(progress), "unit": "%"},
 					  "err_rms": {"value": err_rms, "unit": ""}})
@@ -174,6 +171,7 @@ class Optimizer:
 		forward = None
 		training_remain = batch.shape[0]
 		batch_row_index = 0
+
 		while training_remain > 0:
 			batch_size = min(self.params.mini_batch_size, training_remain)
 			if batch_size == 0: