diff --git a/pts/trainer.py b/pts/trainer.py
index f107c0f..45a6eb3 100644
--- a/pts/trainer.py
+++ b/pts/trainer.py
@@ -23,10 +23,7 @@ class Trainer:
         num_batches_per_epoch: int = 50,
         learning_rate: float = 1e-3,
         weight_decay: float = 1e-6,
-        learning_rate_decay_factor: float = 0.5,
-        patience: int = 10,
-        minimum_learning_rate: float = 5e-5,
-        maximum_learning_rate: float = 0.01,
+        maximum_learning_rate: float = 1e-2,
         clip_gradient: Optional[float] = None,
         device: Optional[Union[torch.device, str]] = None,
         **kwargs,
@@ -36,9 +33,6 @@ class Trainer:
         self.num_batches_per_epoch = num_batches_per_epoch
         self.learning_rate = learning_rate
         self.weight_decay = weight_decay
-        self.learning_rate_decay_factor = learning_rate_decay_factor
-        self.patience = patience
-        self.minimum_learning_rate = minimum_learning_rate
         self.maximum_learning_rate = maximum_learning_rate
         self.clip_gradient = clip_gradient
         self.device = device
@@ -64,13 +58,6 @@ class Trainer:
             steps_per_epoch=self.num_batches_per_epoch,
             epochs=self.epochs,
         )
-        # lr_scheduler = ReduceLROnPlateau(
-        #     optimizer, 
-        #     mode='min',
-        #     factor=self.learning_rate_decay_factor, 
-        #     patience=self.patience,
-        #     min_lr=self.minimum_learning_rate,
-        # )
 
         for epoch_no in range(self.epochs):
             # mark epoch start time
@@ -101,11 +88,11 @@ class Trainer:
                     loss.backward()
                     if self.clip_gradient is not None:
                         nn.utils.clip_grad_norm_(net.parameters(), self.clip_gradient)
+                    
                     optimizer.step()
                     lr_scheduler.step()
 
                     if self.num_batches_per_epoch == batch_no:
-                        # lr_scheduler.step(avg_epoch_loss / batch_no)
                         break
 
             # mark epoch end time and log time cost of current epoch