revised status

src/rbm/layer.py

@@ -34,8 +34,8 @@ class Layer:
 		if batch_size == 0:
 			batch_size = training_remain
 
+		status.on_change({})
 		d_progress = 100.0 / (training_remain/batch_size * self.params.num_epochs)
-		last_progress = 0
 		batch_row_index = 0
 		training_seen = 0
 		keep_running = True
@@ -67,19 +67,15 @@ class Layer:
 				self.state.b_h += inc_bh
 				self.state.w_hv += inc_whv
 
-				progress = round(training_seen*d_progress)
-				if progress != last_progress:
-					# Calculate	error
-					status.progress = round(progress)
-					status.err = rms_error_accu(mini_batch - self.h_to_pv(self.v_to_ph(v_states)))
-					if not status.on_change():
-						keep_running = False
-						break
+				# 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": "%"}, "err_rms": {"value": err_rms, "unit": ""}}):
+					keep_running = False
+					break
 
-				last_progress = progress
 				training_seen += 1
 
-		status.on_change()
+		status.on_change({"progress": {"value": round(training_seen*d_progress), "unit": "%"}, "err_rms": {"value": err_rms, "unit": ""}})
 
 	def v_to_ph(self, v: np.ndarray) -> np.ndarray:
 		state = self.state.v_to_h(v)