diff --git a/src/train.sh b/src/train.sh
index a03b487..85e37a5 100755
--- a/src/train.sh
+++ b/src/train.sh
@@ -11,5 +11,5 @@ MODELDIR="../model-$NETWORK-$JOB"
 mkdir -p "$MODELDIR"
 PREFIX="$MODELDIR/model"
 LOGFILE="$MODELDIR/log"
-CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --data-dir $DATA_DIR --network "$NETWORK" --loss-type 0 --lr 0.1 --prefix "$PREFIX" --per-batch-size 128 --image-size '112,112' --version-input 1 --version-output E --version-unit 3 > "$LOGFILE" 2>&1 &
+CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --data-dir $DATA_DIR --network "$NETWORK" --loss-type 0 --lr 0.1 --prefix "$PREFIX" --per-batch-size 128 --image-size '112,112' --version-input 1 --version-output E --version-unit 3 --use-se > "$LOGFILE" 2>&1 &
 
diff --git a/src/train_softmax.py b/src/train_softmax.py
index e2435ba..1d06fb8 100644
--- a/src/train_softmax.py
+++ b/src/train_softmax.py
@@ -504,8 +504,9 @@ def train_net(args):
       print('VACC: %f'%(acc_value))
 
 
-    highest_acc = [0.0]
-    last_save_acc = [0.0]
+    highest_acc = []
+    for i in xrange(len(ver_list)):
+      highest_acc.append(0.0)
     global_step = [0]
     save_step = [0]
     if len(args.lr_steps)==0:
@@ -534,15 +535,17 @@ def train_net(args):
 
       if mbatch>=0 and mbatch%args.verbose==0:
         acc_list = ver_test(mbatch)
-        acc = acc_list[0]
         save_step[0]+=1
         msave = save_step[0]
         do_save = False
-        if acc>=highest_acc[0]:
-          highest_acc[0] = acc
-          if acc>=0.99:
-            do_save = True
-        if mbatch>lr_steps[-1] and mbatch%10000==0:
+        lfw_score = acc_list[0]
+        for i in xrange(len(acc_list)):
+          acc = acc_list[i]
+          if acc>=highest_acc[i]:
+            highest_acc[i] = acc
+            if lfw_score>=0.99:
+              do_save = True
+        if args.loss_type==1 and mbatch>lr_steps[-1] and mbatch%10000==0:
           do_save = True
         if do_save:
           print('saving', msave, acc)
@@ -555,13 +558,12 @@ def train_net(args):
           #  X = np.concatenate(embeddings_list, axis=0)
           #  print('saving lfw npy', X.shape)
           #  np.save(lfw_npy, X)
-        print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[0]))
+        #print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[0]))
       if mbatch<=args.beta_freeze:
         _beta = args.beta
       else:
         move = max(0, mbatch-args.beta_freeze)
         _beta = max(args.beta_min, args.beta*math.pow(1+args.gamma*move, -1.0*args.power))
-        #_beta = max(args.beta_min, args.beta*math.pow(0.7, move//500))
       #print('beta', _beta)
       os.environ['BETA'] = str(_beta)