diff --git a/src/data.py b/src/data.py
index e726e15..72e29f6 100644
--- a/src/data.py
+++ b/src/data.py
@@ -114,6 +114,7 @@ class FaceImageIter(io.DataIter):
           self.triplet_mode = True
           self.triplet_oseq_cur = 0
           self.triplet_oseq_reset()
+          self.seq_min_size = self.batch_size*2
         self.cur = 0
         self.is_init = False
         #self.reset()
@@ -180,74 +181,69 @@ class FaceImageIter(io.DataIter):
 
 
     def select_triplets(self):
-      self.triplet_index = 0
-      self.triplets = []
-      embeddings = None
-      bag_size = self.triplet_bag_size
-      batch_size = self.batch_size
-      #data = np.zeros( (bag_size,)+self.data_shape )
-      #label = np.zeros( (bag_size,) )
-      tag = []
-      #idx = np.zeros( (bag_size,) )
-      print('eval %d images..'%bag_size, self.triplet_oseq_cur)
-      if self.triplet_oseq_cur+bag_size>len(self.oseq):
-        self.triplet_oseq_reset()
-      print('eval %d images..'%bag_size, self.triplet_oseq_cur)
-      #print(data.shape)
-      data = nd.zeros( self.provide_data[0][1] )
-      label = nd.zeros( self.provide_label[0][1] )
-      ba = 0
-      while True:
-        bb = min(ba+batch_size, bag_size)
-        if ba>=bb:
-          break
-        #_batch = self.data_iter.next()
-        #_data = _batch.data[0].asnumpy()
-        #print(_data.shape)
-        #_label = _batch.label[0].asnumpy()
-        #data[ba:bb,:,:,:] = _data
-        #label[ba:bb] = _label
-        for i in xrange(ba, bb):
-          _idx = self.oseq[i+self.triplet_oseq_cur]
-          s = self.imgrec.read_idx(_idx)
-          header, img = recordio.unpack(s)
-          img = self.imdecode(img)
-          data[i-ba][:] = self.postprocess_data(img)
-          label[i-ba][:] = header.label
-          tag.append( ( int(header.label), _idx) )
-          #idx[i] = _idx
+      self.seq = []
+      while len(self.seq)<self.seq_min_size:
+        embeddings = None
+        bag_size = self.triplet_bag_size
+        batch_size = self.batch_size
+        #data = np.zeros( (bag_size,)+self.data_shape )
+        #label = np.zeros( (bag_size,) )
+        tag = []
+        #idx = np.zeros( (bag_size,) )
+        print('eval %d images..'%bag_size, self.triplet_oseq_cur)
+        if self.triplet_oseq_cur+bag_size>len(self.oseq):
+          self.triplet_oseq_reset()
+        print('eval %d images..'%bag_size, self.triplet_oseq_cur)
+        #print(data.shape)
+        data = nd.zeros( self.provide_data[0][1] )
+        label = nd.zeros( self.provide_label[0][1] )
+        ba = 0
+        while True:
+          bb = min(ba+batch_size, bag_size)
+          if ba>=bb:
+            break
+          #_batch = self.data_iter.next()
+          #_data = _batch.data[0].asnumpy()
+          #print(_data.shape)
+          #_label = _batch.label[0].asnumpy()
+          #data[ba:bb,:,:,:] = _data
+          #label[ba:bb] = _label
+          for i in xrange(ba, bb):
+            _idx = self.oseq[i+self.triplet_oseq_cur]
+            s = self.imgrec.read_idx(_idx)
+            header, img = recordio.unpack(s)
+            img = self.imdecode(img)
+            data[i-ba][:] = self.postprocess_data(img)
+            label[i-ba][:] = header.label
+            tag.append( ( int(header.label), _idx) )
+            #idx[i] = _idx
 
-        db = mx.io.DataBatch(data=(data,), label=(label,))
-        self.mx_model.forward(db, is_train=False)
-        net_out = self.mx_model.get_outputs()
-        #print('eval for selecting triplets',ba,bb)
-        #print(net_out)
-        #print(len(net_out))
-        #print(net_out[0].asnumpy())
-        net_out = net_out[0].asnumpy()
-        #print(net_out)
-        #print('net_out', net_out.shape)
-        if embeddings is None:
-          embeddings = np.zeros( (bag_size, net_out.shape[1]))
-        embeddings[ba:bb,:] = net_out
-        ba = bb
-      assert len(tag)==bag_size
-      self.triplet_oseq_cur+=bag_size
-      embeddings = sklearn.preprocessing.normalize(embeddings)
-      nrof_images_per_class = [1]
-      for i in xrange(1, bag_size):
-        if tag[i][0]==tag[i-1][0]:
-          nrof_images_per_class[-1]+=1
-        else:
-          nrof_images_per_class.append(1)
-        
-      triplets = self.pick_triplets(embeddings, nrof_images_per_class) # shape=(T,3)
-      if len(triplets)==0:
-        print('triplets 0, retry...')
-        self.select_triplets()
-      else:
-        print('triplets', len(triplets))
-        self.seq = []
+          db = mx.io.DataBatch(data=(data,), label=(label,))
+          self.mx_model.forward(db, is_train=False)
+          net_out = self.mx_model.get_outputs()
+          #print('eval for selecting triplets',ba,bb)
+          #print(net_out)
+          #print(len(net_out))
+          #print(net_out[0].asnumpy())
+          net_out = net_out[0].asnumpy()
+          #print(net_out)
+          #print('net_out', net_out.shape)
+          if embeddings is None:
+            embeddings = np.zeros( (bag_size, net_out.shape[1]))
+          embeddings[ba:bb,:] = net_out
+          ba = bb
+        assert len(tag)==bag_size
+        self.triplet_oseq_cur+=bag_size
+        embeddings = sklearn.preprocessing.normalize(embeddings)
+        nrof_images_per_class = [1]
+        for i in xrange(1, bag_size):
+          if tag[i][0]==tag[i-1][0]:
+            nrof_images_per_class[-1]+=1
+          else:
+            nrof_images_per_class.append(1)
+          
+        triplets = self.pick_triplets(embeddings, nrof_images_per_class) # shape=(T,3)
+        print('found triplets', len(triplets))
         ba = 0
         while True:
           bb = ba+self.per_batch_size//3
diff --git a/src/train_softmax.py b/src/train_softmax.py
index 5d5f30c..e158de4 100644
--- a/src/train_softmax.py
+++ b/src/train_softmax.py
@@ -484,7 +484,7 @@ def train_net(args):
           label_names   = (label_name,),
       )
 
-    if len(data_dir_list)==1:
+    if len(data_dir_list)==1 and args.loss_type!=12:
       train_dataiter = FaceImageIter(
           batch_size           = args.batch_size,
           data_shape           = data_shape,