diff --git a/easy_rec/python/layers/keras/mask_net.py b/easy_rec/python/layers/keras/mask_net.py
index de5624944..92077b82f 100644
--- a/easy_rec/python/layers/keras/mask_net.py
+++ b/easy_rec/python/layers/keras/mask_net.py
@@ -31,6 +31,7 @@ def __init__(self, params, name='mask_block', reuse=None, **kwargs):
     self._projection_dim = params.get_or_default('projection_dim', None)
     self.reuse = reuse
     self.final_relu = Activation('relu', name='relu')
+    self._name = name
 
   def build(self, input_shape):
     assert len(input_shape) >= 2, 'MaskBlock must has at least two inputs'
@@ -49,30 +50,30 @@ def build(self, input_shape):
         activation='relu',
         kernel_initializer='he_uniform',
         kernel_regularizer=self.l2_reg,
-        name='aggregation')
+        name='%s/aggregation' % self._name)
     self.weight_layer = Dense(input_dim, name='weights')
     if self._projection_dim is not None:
       self.project_layer = Dense(
           self._projection_dim,
           kernel_regularizer=self.l2_reg,
           use_bias=False,
-          name='project')
+          name='%s/project' % self._name)
     if self.config.input_layer_norm:
       # 推荐在调用MaskBlock之前做好 layer norm，否则每一次调用都需要对input做ln
       if tf.__version__ >= '2.0':
         self.input_layer_norm = tf.keras.layers.LayerNormalization(
-            name='input_ln')
+            name='%s/input_ln' % self._name)
       else:
-        self.input_layer_norm = LayerNormalization(name='input_ln')
+        self.input_layer_norm = LayerNormalization(name='%s/input_ln' % self._name)
 
     if self.config.HasField('output_size'):
       self.output_layer = Dense(
-          self.config.output_size, use_bias=False, name='output')
+          self.config.output_size, use_bias=False, name='%s/output' % self._name)
     if tf.__version__ >= '2.0':
       self.output_layer_norm = tf.keras.layers.LayerNormalization(
           name='output_ln')
     else:
-      self.output_layer_norm = LayerNormalization(name='output_ln')
+      self.output_layer_norm = LayerNormalization(name='%s/output_ln' % self._name)
 
   def call(self, inputs, **kwargs):
     net, mask_input = inputs
@@ -104,6 +105,7 @@ class MaskNet(Layer):
 
   def __init__(self, params, name='mask_net', reuse=None, **kwargs):
     super(MaskNet, self).__init__(name, **kwargs)
+    self._name = name
     self.reuse = reuse
     self.params = params
     self.config = params.get_pb_config()
@@ -118,15 +120,15 @@ def __init__(self, params, name='mask_net', reuse=None, **kwargs):
     for i, block_conf in enumerate(self.config.mask_blocks):
       params = Parameter.make_from_pb(block_conf)
       params.l2_regularizer = self.params.l2_regularizer
-      mask_layer = MaskBlock(params, name='block_%d' % i, reuse=self.reuse)
+      mask_layer = MaskBlock(params, name='%s/block_%d' % (self._name, i), reuse=self.reuse)
       self.mask_layers.append(mask_layer)
 
     if self.config.input_layer_norm:
       if tf.__version__ >= '2.0':
         self.input_layer_norm = tf.keras.layers.LayerNormalization(
-            name='input_ln')
+            name='%s/input_ln' % self._name)
       else:
-        self.input_layer_norm = LayerNormalization(name='input_ln')
+        self.input_layer_norm = LayerNormalization(name='%s/input_ln' % self._name)
 
   def call(self, inputs, training=None, **kwargs):
     if self.config.input_layer_norm: