diff --git a/image_ref/main.py b/image_ref/main.py
index 2b53852eb9671980350c6c4b77b5f8851734c017..1c3299b77c2e33b491d545cbc1ba25fb82d26710 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -214,7 +214,6 @@ def make_prediction_duo(model, data, f_name, f_name2):
     confidence_pred_list = [[] for i in range(n_class)]
     y_pred = []
     y_true = []
-    soft_max = nn.Softmax(dim=1)
     # iterate over test data
     for imaer, imana, img_ref, label in data:
         imaer = imaer.float()
@@ -234,13 +233,12 @@ def make_prediction_duo(model, data, f_name, f_name2):
             imana = imana.cuda()
             img_ref = img_ref.cuda()
             label = label.cuda()
-        output = model(imaer, imana, img_ref)
-        confidence = soft_max(output)
+        confidence = model(imaer, imana, img_ref)
         print(label)
         print(confidence)
         confidence_pred_list[specie].append(confidence[:, 0].data.cpu().numpy())
         # Mono class output (only most postive paire)
-        output = torch.argmax(output[:, 0])
+        output = torch.argmax(confidence[:, 0])
         label = torch.argmin(label)
         y_pred.append(output.tolist())
         y_true.append(label.tolist())  # Save Truth
diff --git a/image_ref/model.py b/image_ref/model.py
index 5302e3b122d79d71372a781aee834f805840f5a4..0f702fb60cd5c8b96bf580b720b8d77a47f705c9 100644
--- a/image_ref/model.py
+++ b/image_ref/model.py
@@ -287,7 +287,7 @@ class Classification_model_duo_contrastive(nn.Module):
             self.im_encoder = resnet34(num_classes=2, in_channels=2)
 
         self.predictor = nn.Linear(in_features=2*2,out_features=2)
-
+        self.soft_max = nn.Softmax(dim=1)
 
     def forward(self, input_aer, input_ana, input_ref):
         input_ana = torch.concat([input_ana, input_ref], dim=1)
@@ -295,4 +295,5 @@ class Classification_model_duo_contrastive(nn.Module):
         out_aer =  self.im_encoder(input_aer)
         out_ana = self.im_encoder(input_ana)
         out = torch.concat([out_aer,out_ana],dim=1)
-        return self.predictor(out)
\ No newline at end of file
+        out = self.predictor(out)
+        return self.soft_max(out)
\ No newline at end of file