diff --git a/model/model.py b/model/model.py
index be5d10f..9b816fb 100644
--- a/model/model.py
+++ b/model/model.py
@@ -663,9 +663,8 @@ def __init__(self, expert_dims, text_dim, use_ce, verbose, l2renorm, num_classes
             self.text_GU = nn.ModuleList(gated_text_embds)
         else:
             print("V. simple classifier, should update....")
-            total_dim = 0
-            for mod in self.expert_dims.keys():
-                total_dim += self.expert_dims[mod][1] * self.repeat_temporal[mod]
+            total_dim = np.dot([self.expert_dims[mod][1] for mod in self.expert_dims.keys()], 
+                               [self.repeat_temporal[mod]  for mod in self.expert_dims.keys()])
             print(f"Total dim is {total_dim}")
             self.classifier = nn.Linear(total_dim, self.num_classes)
 
@@ -767,11 +766,9 @@ def forward(self, text, experts, ind, raw_captions):
 
             if self.use_ce in {"pairwise-star", "pairwise-star-specific",
                                "pairwise-star-tensor"}:
-                sum_all = 0
-                sum_ind = 0
-                for mod0 in experts.keys():
-                    sum_all += (experts[mod0] * ind[mod0].float().to(dev).unsqueeze(1))
-                    sum_ind += ind[mod0].float().to(dev).unsqueeze(1)
+                sum_all = np.dot([experts[mod0] for mod0 in experts.keys()], 
+                                 [ind[mod0].float().to(dev).unsqueeze(1)) for mod0 in experts.keys()])
+                sum_ind = np.sum([ind[mod0].float().to(dev).unsqueeze(1)   for mod0 in experts.keys()] )
                 avg_modality = sum_all / sum_ind
 
             for ii, l in enumerate(self.video_GU):