fixed progress indication

src/rbm/entity.py

@@ -11,15 +11,15 @@ class Entity:
 		self.params = params
 
 	def train(self, batch: np.ndarray, cd_func: Callable, status: Status):
-		training_remain = batch.shape[0]
+		training_remain = batch.shape[1]
 		batch_size = min(self.params.mini_batch_size, training_remain)
 		if batch_size == 0:
 			batch_size = training_remain
 
 		status.on_change()
-		d_progress = 100.0 / (training_remain/batch_size * self.params.num_epochs)
+		d_progress = 100.0 / (training_remain*self.params.num_epochs)
+		progress = 0
 		batch_row_index = 0
-		training_seen = 0
 		keep_running = True
 		while training_remain > 0 and keep_running:
 			batch_size_remain = min(batch_size, training_remain)
@@ -50,15 +50,15 @@ class Entity:
 				self.state.w_hv += inc_whv
 
 				# check if status update is needed
-				if status.want_report(round(training_seen*d_progress)):
+				if status.want_report(round(progress)):
 					# Calculate	error
 					err_rms = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states)))
-					if not status.on_change({"progress": {"value": round(training_seen * d_progress), "unit": "%"},
+					if not status.on_change({"progress": {"value": round(progress), "unit": "%"},
 											 "err_rms": {"value": err_rms, "unit": ""}}):
 						keep_running = False
 						break
 
-				training_seen += 1
+				progress += d_progress*batch_size_remain
 
 	def v_to_ph(self, v: np.ndarray) -> np.ndarray:
 		state = self.state.v_to_h(v)