diff --git a/Deep-SAD-PyTorch/src/optim/DeepSAD_trainer.py b/Deep-SAD-PyTorch/src/optim/DeepSAD_trainer.py
index 599fdc1..57d273d 100644
--- a/Deep-SAD-PyTorch/src/optim/DeepSAD_trainer.py
+++ b/Deep-SAD-PyTorch/src/optim/DeepSAD_trainer.py
@@ -83,13 +83,6 @@ class DeepSADTrainer(BaseTrainer):
         net.train()
         for epoch in range(self.n_epochs):
 
-            scheduler.step()
-            if epoch in self.lr_milestones:
-                logger.info(
-                    "  LR scheduler: new learning rate is %g"
-                    % float(scheduler.get_lr()[0])
-                )
-
             epoch_loss = 0.0
             n_batches = 0
             epoch_start_time = time.time()
@@ -117,6 +110,13 @@ class DeepSADTrainer(BaseTrainer):
                 epoch_loss += loss.item()
                 n_batches += 1
 
+            scheduler.step()
+            if epoch in self.lr_milestones:
+                logger.info(
+                    "  LR scheduler: new learning rate is %g"
+                    % float(scheduler.get_lr()[0])
+                )
+
             # log epoch statistics
             epoch_train_time = time.time() - epoch_start_time
             logger.info(
diff --git a/Deep-SAD-PyTorch/src/optim/ae_trainer.py b/Deep-SAD-PyTorch/src/optim/ae_trainer.py
index e612e25..e8a8d30 100644
--- a/Deep-SAD-PyTorch/src/optim/ae_trainer.py
+++ b/Deep-SAD-PyTorch/src/optim/ae_trainer.py
@@ -71,13 +71,6 @@ class AETrainer(BaseTrainer):
         ae_net.train()
         for epoch in range(self.n_epochs):
 
-            scheduler.step()
-            if epoch in self.lr_milestones:
-                logger.info(
-                    "  LR scheduler: new learning rate is %g"
-                    % float(scheduler.get_lr()[0])
-                )
-
             epoch_loss = 0.0
             n_batches = 0
             epoch_start_time = time.time()
@@ -98,6 +91,13 @@ class AETrainer(BaseTrainer):
                 epoch_loss += loss.item()
                 n_batches += 1
 
+            scheduler.step()
+            if epoch in self.lr_milestones:
+                logger.info(
+                    "  LR scheduler: new learning rate is %g"
+                    % float(scheduler.get_last_lr()[0])
+                )
+
             # log epoch statistics
             epoch_train_time = time.time() - epoch_start_time
             logger.info(