diff --git a/train_diffusion_prior.py b/train_diffusion_prior.py
index a45199d..3c2bf80 100644
--- a/train_diffusion_prior.py
+++ b/train_diffusion_prior.py
@@ -17,14 +17,24 @@ os.environ["WANDB_SILENT"] = "true"
 def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
     model.eval()
     with torch.no_grad():
-        for emb_images,emb_text in zip(image_reader(batch_size=batch_size, start=start, end=end),
+        total_loss = 0.
+        total_samples = 0.
+
+        for emb_images, emb_text in zip(image_reader(batch_size=batch_size, start=start, end=end),
                 text_reader(batch_size=batch_size, start=start, end=end)):
+
             emb_images_tensor = torch.tensor(emb_images[0]).to(device)
             emb_text_tensor = torch.tensor(emb_text[0]).to(device)
+
+            batches = emb_images_tensor.shape[0]
+
             loss = model(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
 
-            # Log to wandb
-            wandb.log({f'{phase} {loss_type}': loss})
+            total_loss += loss.item() * batches
+            total_samples += batches
+
+        avg_loss = (total_loss / total_samples)
+        wandb.log({f'{phase} {loss_type}': avg_loss})
 
 def save_model(save_path,state_dict):
     # Saving State Dict