final

@@ -31,10 +31,13 @@ def normalize(arrs):
     normalized_arr = []
     normalized_arr = []
     for x in arrs:
     for x in arrs:
         normalized_arr.append(float(x))
         normalized_arr.append(float(x))
-        
-    maximum = max(normalized_arr)
-    minimum = min(normalized_arr)
-    denom = float(maximum) - float(minimum)
-    for i in range(0,len(normalized_arr)):
-        normalized_arr[i] = ((normalized_arr[i] - minimum)/ denom) * 2 - 1
+    
+    if len(normalized_arr) > 0:    
+        maximum = max(normalized_arr)
+        minimum = min(normalized_arr)
+        denom = float(maximum) - float(minimum)
+        if denom == 0:
+            denom = 1
+        for i in range(0,len(normalized_arr)):
+            normalized_arr[i] = ((normalized_arr[i] - minimum)/ denom) * 2 - 1
     return normalized_arr
     return normalized_arr
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@@ -230,7 +230,7 @@ def inference_pb(file_path, threshold):
         
         
         # 5. Create recommend videos info, Combine results.
         # 5. Create recommend videos info, Combine results.
         recommend_video_ids = recommender.recommend_videos(tag_result, inputVideoTagResults, TAG_VECTOR_MODEL_PATH,
         recommend_video_ids = recommender.recommend_videos(tag_result, inputVideoTagResults, TAG_VECTOR_MODEL_PATH,
-                                                           VIDEO_VECTOR_MODEL_PATH, VIDEO_ID_MODEL_PATH, VIDEO_TOP_K)
+                                                           VIDEO_VECTOR_MODEL_PATH, VIDEO_ID_MODEL_PATH, VIDEO_TOP_K,True)
         video_result = [videoutil.getVideoInfo(ids, VIDEO_TAGS_PATH, TAG_TOP_K,False) for ids in recommend_video_ids]
         video_result = [videoutil.getVideoInfo(ids, VIDEO_TAGS_PATH, TAG_TOP_K,False) for ids in recommend_video_ids]
 
 
         inference_result = {
         inference_result = {

+결과
+커널크기/비디오 수/캡2/캡1
+1/260/29/31 -> 129/131
+3/260/32/30 -> 131/129
+5/260/36/24 -> 136/124
+7/260/35/25 -> 135/125
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