diff --git a/src/symbols/fresnet.py b/src/symbols/fresnet.py
index e632e3a..0fbf5c6 100644
--- a/src/symbols/fresnet.py
+++ b/src/symbols/fresnet.py
@@ -291,7 +291,7 @@ def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck=True
         return bn3 + shortcut
 
 def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
-  return residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, use_se, bn_mom, workspace, memonger)
+  return residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, use_se, bn_mom, workspace, memonger)
 
 def resnet(units, num_stages, filter_list, num_classes, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
     """Return ResNet symbol of
@@ -310,6 +310,8 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck=True, use_se
     workspace : int
         Workspace used in convolution operator
     """
+    L_type = False
+    fc_type = 'B'#'A'-'E'
     num_unit = len(units)
     assert(num_unit == num_stages)
     data = mx.sym.Variable(name='data')
@@ -317,41 +319,48 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck=True, use_se
     data = data-127.5
     data = data*0.0078125
     #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
-    #body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
-    #                          no_bias=True, name="conv0", workspace=workspace)
-    body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1),
-                              no_bias=True, name="conv0", workspace=workspace)
+    if not L_type:
+      body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
+                                no_bias=True, name="conv0", workspace=workspace)
+    else:
+      body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1),
+                                no_bias=True, name="conv0", workspace=workspace)
     body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
     body = Act(data=body, act_type='relu', name='relu0')
-    #body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
+    body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
 
     for i in range(num_stages):
-      #body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
-      #                     name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
-      #                     memonger=memonger)
-      body = residual_unit(body, filter_list[i+1], (2, 2), False,
-        name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
-        memonger=memonger)
+      body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
+                           name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
+                           memonger=memonger)
+      #body = residual_unit(body, filter_list[i+1], (2, 2), False,
+      #  name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
+      #  memonger=memonger)
       for j in range(units[i]-1):
         body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i+1, j+2),
           bottle_neck=bottle_neck, use_se=use_se, workspace=workspace, memonger=memonger)
-      #body = residual_unit(body, filter_list[i+1], (2,2), False, name='stage%d_unit%d' % (i+1, units[i]),
-      #  bottle_neck=bottle_neck, use_se=use_se, workspace=workspace, memonger=memonger)
-    fc_type = 1#0 or 1
 
-    if fc_type==0:
+
+    if fc_type=='E':
+      body = mx.symbol.Dropout(data=body, p=0.4)
+      fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
+      fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
+    else:
       bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
       relu1 = Act(data=bn1, act_type='relu', name='relu1')
       # Although kernel is not used here when global_pool=True, we should put one
       pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
       flat = mx.sym.Flatten(data=pool1)
-      #fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
-      fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
-      fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
-    else:
-      body = mx.symbol.Dropout(data=body, p=0.4)
-      fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
-      fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
+      if fc_type=='A':
+        fc1 = flat
+      else:
+        #B
+        fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
+        if fc_type=='C':
+          fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
+        elif fc_type=='D':
+          fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
+          fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
     return fc1
 
 def get_symbol(num_classes, num_layers, conv_workspace=256, **kwargs):