add test case

docs/source/intro.md

@@ -61,4 +61,6 @@ EasyRec implements state of the art machine learning models used in common recom
 
 - Run [`knn algorithm`](vector_retrieve.md) of vectors in distribute environment
 
-欢迎加入【EasyRec推荐算法交流群】，钉钉群号 : 32260796
+### Contact
+
+- DingDing Group: 37930014162, click [this url](https://qr.dingtalk.com/action/joingroup?code=v1,k1,oHNqtNObbu+xUClHh77gCuKdGGH8AYoQ8AjKU23zTg4=&_dt_no_comment=1&origin=11) or scan QrCode to join![new_group.jpg](../images/qrcode/new_group.jpg)

easy_rec/python/layers/keras/fibinet.py

@@ -36,7 +36,8 @@ def __init__(self, params, name='SENet', reuse=None, **kwargs):
     if tf.__version__ >= '2.0':
       self.layer_norm = tf.keras.layers.LayerNormalization()
     else:
-      self.layer_norm = lambda x: layer_norm(
+      with tf.name_scope(self.name):
+        self.layer_norm = lambda x: layer_norm(
           x, name='ln_output', reuse=self.reuse)
 
   def build(self, input_shape):
@@ -52,13 +53,14 @@ def build(self, input_shape):
     r = self.config.reduction_ratio
     field_size = len(input_shape)
     reduction_size = max(1, field_size * g * 2 // r)
+    name_scope = '' if tf.__version__ >= '2.0' else self.name + "/"
     self.reduce_layer = Dense(
         units=reduction_size,
         activation='relu',
         kernel_initializer='he_normal',
-        name='W1')
+        name=name_scope + 'W1')
     self.excite_layer = Dense(
-        units=emb_size, kernel_initializer='glorot_normal', name='W2')
+        units=emb_size, kernel_initializer='glorot_normal', name=name_scope + 'W2')
 
   def call(self, inputs, **kwargs):
     g = self.config.num_squeeze_group
@@ -137,7 +139,7 @@ def __init__(self, params, name='bilinear', reuse=None, **kwargs):
       self.func = _full_interaction
     else:
       self.func = tf.multiply
-    self.output_layer = Dense(self.output_size, name='output')
+    self.output_layer = Dense(self.output_size, name=self.name + '/output')
 
   def build(self, input_shape):
     if type(input_shape) not in (tuple, list):
@@ -158,16 +160,17 @@ def build(self, input_shape):
       )
     dim = int(_dim)
 
+    name_scope = '' if tf.__version__ >= '2.0' else self.name + "/"
     if self.bilinear_type == 'all':
-      self.dot_layer = Dense(dim, name='all')
+      self.dot_layer = Dense(dim, name=name_scope + 'all')
     elif self.bilinear_type == 'each':
       self.dot_layers = [
-          Dense(dim, name='each_%d' % i) for i in range(field_num - 1)
+          Dense(dim, name=name_scope + 'each_%d' % i) for i in range(field_num - 1)
       ]
     else:  # interaction
       self.dot_layers = [
           Dense(
-              units=int(input_shape[j][-1]), name='interaction_%d_%d' % (i, j))
+              units=int(input_shape[j][-1]), name=name_scope+'interaction_%d_%d' % (i, j))
           for i, j in itertools.combinations(range(field_num), 2)
       ]
 
@@ -216,17 +219,17 @@ def __init__(self, params, name='fibinet', reuse=None, **kwargs):
     self._config = params.get_pb_config()
 
     se_params = Parameter.make_from_pb(self._config.senet)
-    self.senet_layer = SENet(se_params, name='senet', reuse=self.reuse)
+    self.senet_layer = SENet(se_params, name=self.name+'/senet', reuse=self.reuse)
 
     if self._config.HasField('bilinear'):
       bi_params = Parameter.make_from_pb(self._config.bilinear)
       self.bilinear_layer = BiLinear(
-          bi_params, name='bilinear', reuse=self.reuse)
+          bi_params, name=self.name+'/bilinear', reuse=self.reuse)
 
     if self._config.HasField('mlp'):
       p = Parameter.make_from_pb(self._config.mlp)
       p.l2_regularizer = params.l2_regularizer
-      self.final_mlp = MLP(p, name=name, reuse=reuse)
+      self.final_mlp = MLP(p, name=self.name+'/mlp', reuse=reuse)
     else:
       self.final_mlp = None
 

easy_rec/python/layers/keras/mask_net.py

@@ -42,37 +42,40 @@ def build(self, input_shape):
       raise ValueError(
           'Need one of reduction factor or aggregation size for MaskBlock.')
 
+    name_scope = '' if tf.__version__ >= '2.0' else self.name + "/"
     self.weight_layer = Dense(
         aggregation_size,
         activation='relu',
         kernel_initializer='he_uniform',
         kernel_regularizer=self.l2_reg,
-        name='weight')
-    self.mask_layer = Dense(input_dim, name='mask')
+        name=name_scope + 'weight')
+    self.mask_layer = Dense(input_dim, name=name_scope+'mask')
     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=name_scope + 'project')
     if self.config.input_layer_norm:
       # 推荐在调用MaskBlock之前做好 layer norm，否则为了reuse layer参数会产生scope之外的name
       if tf.__version__ >= '2.0':
         self.input_layer_norm = tf.keras.layers.LayerNormalization()
         self.output_layer_norm = tf.keras.layers.LayerNormalization()
       else:
+        # to share input layer norm parameters
         idx = self.name.rfind('_')
         if idx > 0 and self.name[idx + 1:].isdigit():
           input_name = self.name[:idx]
         else:
           input_name = self.name
         self.input_layer_norm = lambda x: layer_norm(
             x, name=input_name + '/input_ln', reuse=tf.AUTO_REUSE)
-        self.output_layer_norm = lambda x: layer_norm(
+        with tf.name_scope(self.name):
+          self.output_layer_norm = lambda x: layer_norm(
             x, name='ln_output', reuse=self.reuse)
     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=name_scope + 'output')
 
   def call(self, inputs, **kwargs):
     net, mask_input = inputs