diff --git a/mmdnn/conversion/darknet/darknet_graph.py b/mmdnn/conversion/darknet/darknet_graph.py
index 9c782a1a..d480d869 100644
--- a/mmdnn/conversion/darknet/darknet_graph.py
+++ b/mmdnn/conversion/darknet/darknet_graph.py
@@ -68,14 +68,14 @@ def conv_output_height(height, padding, kernel_size, stride):
         return (height + 2*padding - kernel_size)/stride + 1
 
 
-    def activation(self, block, pre_node_name, input_shape):
+    def activation(self, block, pre_node_name, input_shape, id):
         if block['activation'] != 'linear':
             relu_layer = OrderedDict()
             relu_layer['input'] = [pre_node_name]
             if 'name' in block.keys():
                 relu_layer['name'] = '%s-act' % block['name']
             else:
-                relu_layer['name'] = 'layer%d-act' % i
+                relu_layer['name'] = 'layer%d-act' % id
             relu_layer['type'] = 'ReLU'
             relu_param = OrderedDict()
             if block['activation'] == 'leaky':
@@ -180,7 +180,7 @@ def build(self):
 
                     pre_node_name = bn_layer['name']
 
-                pre_node_name = self.activation(block, pre_node_name, input_shape)
+                pre_node_name = self.activation(block, pre_node_name, input_shape, i)
 
 
             elif block['type'] == 'maxpool':
@@ -301,7 +301,7 @@ def build(self):
                 self.layer_num_map[i] = shortcut_layer['name']
                 pre_node_name = shortcut_layer['name']
 
-                pre_node_name = self.activation(block, pre_node_name, input_shape)
+                pre_node_name = self.activation(block, pre_node_name, input_shape, i)
 
             elif block['type'] == 'connected':
                 fc_layer = OrderedDict()
@@ -321,7 +321,7 @@ def build(self):
                 self.layer_num_map[i] = fc_layer['name']
                 pre_node_name = fc_layer['name']
 
-                pre_node_name = self.activation(block, pre_node_name, input_shape)
+                pre_node_name = self.activation(block, pre_node_name, input_shape, i)
 
             elif block['type'] == 'softmax':
                 sm_layer = OrderedDict()