diff --git a/abl/learning/basic_nn.py b/abl/learning/basic_nn.py
index 8bd92cb..eae6d9e 100644
--- a/abl/learning/basic_nn.py
+++ b/abl/learning/basic_nn.py
@@ -54,6 +54,7 @@ class BasicNN:
         model: torch.nn.Module,
         loss_fn: torch.nn.Module,
         optimizer: torch.optim.Optimizer,
+        scheduler: Optional[torch.optim.lr_scheduler.LRScheduler] = None,
         device: torch.device = torch.device("cpu"),
         batch_size: int = 32,
         num_epochs: int = 1,
@@ -71,6 +72,10 @@ class BasicNN:
             raise TypeError("loss_fn must be an instance of torch.nn.Module")
         if not isinstance(optimizer, torch.optim.Optimizer):
             raise TypeError("optimizer must be an instance of torch.optim.Optimizer")
+        if scheduler is not None and not isinstance(
+            scheduler, torch.optim.lr_scheduler.LRScheduler
+        ):
+            raise TypeError("scheduler must be an instance of torch.optim.lr_scheduler.LRScheduler")
         if not isinstance(device, torch.device):
             raise TypeError("device must be an instance of torch.device")
         if not isinstance(batch_size, int):
@@ -95,6 +100,7 @@ class BasicNN:
         self.model = model.to(device)
         self.loss_fn = loss_fn
         self.optimizer = optimizer
+        self.scheduler = scheduler
         self.device = device
         self.batch_size = batch_size
         self.num_epochs = num_epochs
@@ -144,6 +150,8 @@ class BasicNN:
                 self.save(epoch + 1)
             if self.stop_loss is not None and loss_value < self.stop_loss:
                 break
+        if self.scheduler is not None:
+            self.scheduler.step()
         print_log(f"model loss: {loss_value:.5f}", logger="current")
         return self
 
@@ -208,7 +216,9 @@ class BasicNN:
         for data, target in data_loader:
             data, target = data.to(device), target.to(device)
             out = model(data)
-            loss = loss_fn(out, target)
+            proba = torch.nn.functional.softmax(out, dim=1)
+            entropy = -torch.sum(proba * torch.log(proba + 1e-5), dim=1).mean()
+            loss = loss_fn(out, target) - 0.3 * entropy
 
             optimizer.zero_grad()
             loss.backward()