diff --git a/enhancer/loss.py b/enhancer/loss.py
index 3bc6fa2..ef33161 100644
--- a/enhancer/loss.py
+++ b/enhancer/loss.py
@@ -1,5 +1,3 @@
-from modulefinder import Module
-from turtle import forward
 import torch
 import torch.nn as nn
 
@@ -10,6 +8,7 @@ class mean_squared_error(nn.Module):
         super().__init__()
 
         self.loss_fun = nn.MSELoss(reduction=reduction)
+        self.higher_better = False
 
     def forward(self,prediction:torch.Tensor, target: torch.Tensor):
 
@@ -25,6 +24,7 @@ class mean_absolute_error(nn.Module):
         super().__init__()
 
         self.loss_fun = nn.L1Loss(reduction=reduction)
+        self.higher_better = False
 
     def forward(self, prediction:torch.Tensor, target: torch.Tensor):
 
@@ -45,6 +45,7 @@ class Si_SDR(nn.Module):
             self.reduction = reduction
         else:
             raise TypeError("Invalid reduction, valid options are sum, mean, None")
+        self.higher_better = False
 
     def forward(self,prediction:torch.Tensor, target:torch.Tensor):
 
@@ -76,6 +77,12 @@ class Avergeloss(nn.Module):
         super().__init__()
 
         self.valid_losses = nn.ModuleList()
+        
+        direction = [getattr(LOSS_MAP[loss](),"higher_better") for loss in losses]
+        if len(set(direction)) > 1:
+            raise ValueError("all cost functions should be of same nature, maximize or minimize!")
+
+        self.higher_better = direction[0]
         for loss in losses:
             loss = self.validate_loss(loss)
             self.valid_losses.append(loss())