diff --git a/caliGraph.py b/caliGraph.py
index f968712..20bc8fb 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -428,8 +428,8 @@ def scoreUnread(G, globMu, globStd):
                     #ws.append(['tgb_rank'])
                     feedbacks.append(node['std'])
                     ws.append(['sigma'])
-                    #feedbacks.append(node['median'])
-                    #ws.append(['median'])
+                    feedbacks.append(node['median'])
+                    ws.append(['median'])
                     #feedbacks.append(node['se'])
                     #ws.append(['se'])
                     feedbacks.append(globMu)
@@ -1266,7 +1266,7 @@ def train(initGamma, full=True):
         print({'mse': mse, 'gamma': gamma, 'delta': delta})
         delta = sum(gradient[g]**2 for g in gradient)
         for wt in weights:
-            weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
+            weights[wt] += gamma*0.1*gradient[wt]/math.sqrt(delta)
         mse, gradient = evaluateFitness(books)
         if mse < last_mse:
             gamma = gamma*1.25
@@ -1304,7 +1304,7 @@ def loadWeights():
         with open('neuralWeights.json', 'r') as f:
             weights = json.loads(f.read())
     except IOError:
-        weights = {"topList": 0.15, "recommender": 0.30, "author": 0.70, "series": 0.05, "tag": 0.05, "pagerank": 0.05, "mu": 0.50, "sigma": 0.30, "bias": 0.25} #, "tgb_rank": 0.10}
+        weights = {"topList": 0.15, "recommender": 0.30, "author": 0.70, "series": 0.05, "tag": 0.05, "pagerank": 0.05, "mu": 0.50, "sigma": 0.30, "bias": 0.25, "median": 0.10} #, "tgb_rank": 0.10}
     return weights
 
 def cliInterface():
@@ -1324,6 +1324,7 @@ def cliInterface():
     parser.add_argument('--v3d', action="store_true")
     parser.add_argument('--imgs', action="store_true")
     parser.add_argument('--perf-test', action="store_true")
+    parser.add_argument('--train', action="store_true")
     cmds = parser.add_subparsers(required=True, dest='cmd')
 
     p_rec = cmds.add_parser('recommend', description="TODO", aliases=['rec'])
@@ -1387,6 +1388,9 @@ def mainCLI(args):
         train(args.g, args.full)
         exit()
 
+    if args.train:
+        train(0.2, False)
+
     bestListT = 'book'
 
     G, books = buildFullGraph(darkMode=args.dark)