update

deepctr/layers/activation.py

@@ -40,12 +40,12 @@ def build(self, input_shape):
         self.bn = tf.keras.layers.BatchNormalization(
             axis=self.axis, epsilon=self.epsilon, center=False, scale=False)
         self.alphas = self.add_weight(shape=(input_shape[-1],), initializer=Zeros(
-        ), dtype=tf.float32, name= 'dice_alpha')  # name='alpha_'+self.name
+        ), dtype=tf.float32, name='dice_alpha')  # name='alpha_'+self.name
         super(Dice, self).build(input_shape)  # Be sure to call this somewhere!
         self.uses_learning_phase = True
 
-    def call(self, inputs,training=None,**kwargs):
-        inputs_normed = self.bn(inputs,training=training)
+    def call(self, inputs, training=None, **kwargs):
+        inputs_normed = self.bn(inputs, training=training)
         # tf.layers.batch_normalization(
         # inputs, axis=self.axis, epsilon=self.epsilon, center=False, scale=False)
         x_p = tf.sigmoid(inputs_normed)
@@ -59,9 +59,10 @@ def get_config(self, ):
         base_config = super(Dice, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+
 def activation_layer(activation):
     if activation == "dice" or activation == "Dice":
-        act_layer =  Dice()
+        act_layer = Dice()
     elif (isinstance(activation, str)) or (sys.version_info.major == 2 and isinstance(activation, (str, unicode))):
         act_layer = tf.keras.layers.Activation(activation)
     elif issubclass(activation, Layer):

deepctr/models/afm.py

@@ -11,7 +11,7 @@
 """
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns, get_linear_logit,build_input_features,DEFAULT_GROUP_NAME
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features, DEFAULT_GROUP_NAME
 from ..layers.core import PredictionLayer
 from ..layers.interaction import AFMLayer, FM
 from ..layers.utils import concat_func, add_func
@@ -37,24 +37,25 @@ def AFM(linear_feature_columns, dnn_feature_columns, fm_group=DEFAULT_GROUP_NAME
     :return: A Keras model instance.
     """
 
-
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 
     group_embedding_dict, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
-                                                         seed, support_dense=False,support_group=True)
+                                                         seed, support_dense=False, support_group=True)
 
     linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                     l2_reg=l2_reg_linear)
 
     if use_attention:
         fm_logit = add_func([AFMLayer(attention_factor, l2_reg_att, afm_dropout,
-        seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group])
+                                      seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group])
     else:
-        fm_logit = add_func([FM()(concat_func(v, axis=1)) for k, v in group_embedding_dict.items() if k in fm_group])
+        fm_logit = add_func([FM()(concat_func(v, axis=1))
+                             for k, v in group_embedding_dict.items() if k in fm_group])
 
-    final_logit = add_func([linear_logit,fm_logit])
+    final_logit = add_func([linear_logit, fm_logit])
     output = PredictionLayer(task)(final_logit)
 
     model = tf.keras.models.Model(inputs=inputs_list, outputs=output)

deepctr/models/autoint.py

@@ -80,7 +80,7 @@ def AutoInt(linear_feature_columns, dnn_feature_columns, att_layer_num=3, att_em
     else:  # Error
         raise NotImplementedError
 
-    final_logit = add_func([final_logit,linear_logit])
+    final_logit = add_func([final_logit, linear_logit])
     output = PredictionLayer(task)(final_logit)
 
     model = tf.keras.models.Model(inputs=inputs_list, outputs=output)

deepctr/models/ccpm.py

@@ -11,7 +11,7 @@
 """
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns, get_linear_logit,build_input_features
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features
 from ..layers.core import DNN, PredictionLayer
 from ..layers.sequence import KMaxPooling
 from ..layers.utils import concat_func, add_func
@@ -41,7 +41,8 @@ def CCPM(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5),
         raise ValueError(
             "conv_kernel_width must have same element with conv_filters")
 
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
     inputs_list = list(features.values())
 
     sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
@@ -68,10 +69,10 @@ def CCPM(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5),
 
     flatten_result = tf.keras.layers.Flatten()(pooling_result)
     dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn,
-                      dropout_rate=dnn_dropout)(flatten_result)
+                  dropout_rate=dnn_dropout)(flatten_result)
     dnn_logit = tf.keras.layers.Dense(1, use_bias=False)(dnn_out)
 
-    final_logit = add_func([dnn_logit,linear_logit])
+    final_logit = add_func([dnn_logit, linear_logit])
 
     output = PredictionLayer(task)(final_logit)
     model = tf.keras.models.Model(inputs=inputs_list, outputs=output)

deepctr/models/dcn.py

@@ -8,7 +8,7 @@
 """
 import tensorflow as tf
 
-from deepctr.layers.utils import add_func
+from ..layers.utils import add_func
 from ..inputs import input_from_feature_columns, build_input_features, combined_dnn_input, get_linear_logit
 from ..layers.core import PredictionLayer, DNN
 from ..layers.interaction import CrossNet
@@ -53,14 +53,17 @@ def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_uni
                        dnn_use_bn, seed)(dnn_input)
         cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
         stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out])
-        final_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(stack_out)
+        final_logit = tf.keras.layers.Dense(
+            1, use_bias=False, activation=None)(stack_out)
     elif len(dnn_hidden_units) > 0:  # Only Deep
         deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                        dnn_use_bn, seed)(dnn_input)
-        final_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(deep_out)
+        final_logit = tf.keras.layers.Dense(
+            1, use_bias=False, activation=None)(deep_out)
     elif cross_num > 0:  # Only Cross
         cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
-        final_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(cross_out)
+        final_logit = tf.keras.layers.Dense(
+            1, use_bias=False, activation=None)(cross_out)
     else:  # Error
         raise NotImplementedError
 

deepctr/models/deepfm.py

@@ -11,7 +11,7 @@
 from itertools import chain
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns, get_linear_logit,build_input_features,combined_dnn_input,DEFAULT_GROUP_NAME
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features, combined_dnn_input, DEFAULT_GROUP_NAME
 from ..layers.core import PredictionLayer, DNN
 from ..layers.interaction import FM
 from ..layers.utils import concat_func, Add, add_func
@@ -38,24 +38,27 @@ def DeepFM(linear_feature_columns, dnn_feature_columns, fm_group=[DEFAULT_GROUP_
     :return: A Keras model instance.
     """
 
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 
     group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
-                                                                        init_std, seed,support_group=True)
+                                                                        init_std, seed, support_group=True)
 
     linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                     l2_reg=l2_reg_linear)
-    fm_logit = add_func([FM()(concat_func(v, axis=1)) for k, v in group_embedding_dict.items() if k in fm_group])
+    fm_logit = add_func([FM()(concat_func(v, axis=1))
+                         for k, v in group_embedding_dict.items() if k in fm_group])
 
-    dnn_input = combined_dnn_input(list(chain.from_iterable(group_embedding_dict.values())),dense_value_list)
+    dnn_input = combined_dnn_input(list(chain.from_iterable(
+        group_embedding_dict.values())), dense_value_list)
     dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                   dnn_use_bn, seed)(dnn_input)
+                     dnn_use_bn, seed)(dnn_input)
     dnn_logit = tf.keras.layers.Dense(
         1, use_bias=False, activation=None)(dnn_output)
 
-    final_logit = add_func([linear_logit,fm_logit,dnn_logit])
+    final_logit = add_func([linear_logit, fm_logit, dnn_logit])
 
     output = PredictionLayer(task)(final_logit)
     model = tf.keras.models.Model(inputs=inputs_list, outputs=output)

deepctr/models/fnn.py

@@ -8,12 +8,11 @@
 """
 import tensorflow as tf
 
-from deepctr.layers.utils import add_func
-from ..inputs import input_from_feature_columns, get_linear_logit,build_input_features,combined_dnn_input
+from ..layers.utils import add_func
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features, combined_dnn_input
 from ..layers.core import PredictionLayer, DNN
 
 
-
 def FNN(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128),
         l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0,
         dnn_activation='relu', task='binary'):
@@ -32,7 +31,8 @@ def FNN(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128)
     :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
     :return: A Keras model instance.
     """
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 
@@ -42,12 +42,12 @@ def FNN(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128)
     linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                     l2_reg=l2_reg_linear)
 
-    dnn_input = combined_dnn_input(sparse_embedding_list,dense_value_list)
+    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
     deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                    dnn_dropout, False, seed)(dnn_input)
     dnn_logit = tf.keras.layers.Dense(
         1, use_bias=False, activation=None)(deep_out)
-    final_logit = add_func([dnn_logit,linear_logit])
+    final_logit = add_func([dnn_logit, linear_logit])
 
     output = PredictionLayer(task)(final_logit)
 

deepctr/models/nfm.py

@@ -8,7 +8,7 @@
 """
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features,combined_dnn_input
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features, combined_dnn_input
 from ..layers.core import PredictionLayer, DNN
 from ..layers.interaction import BiInteractionPooling
 from ..layers.utils import concat_func, add_func
@@ -34,7 +34,8 @@ def NFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128)
     :return: A Keras model instance.
     """
 
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 
@@ -48,13 +49,13 @@ def NFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128)
     bi_out = BiInteractionPooling()(fm_input)
     if bi_dropout:
         bi_out = tf.keras.layers.Dropout(bi_dropout)(bi_out, training=None)
-    dnn_input = combined_dnn_input([bi_out],dense_value_list)
+    dnn_input = combined_dnn_input([bi_out], dense_value_list)
     dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                   False, seed)(dnn_input)
+                     False, seed)(dnn_input)
     dnn_logit = tf.keras.layers.Dense(
         1, use_bias=False, activation=None)(dnn_output)
 
-    final_logit = add_func([linear_logit,dnn_logit])
+    final_logit = add_func([linear_logit, dnn_logit])
 
     output = PredictionLayer(task)(final_logit)
 

deepctr/models/pnn.py

@@ -9,7 +9,7 @@
 
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns,build_input_features,combined_dnn_input
+from ..inputs import input_from_feature_columns, build_input_features, combined_dnn_input
 from ..layers.core import PredictionLayer, DNN
 from ..layers.interaction import InnerProductLayer, OutterProductLayer
 from ..layers.utils import concat_func
@@ -45,7 +45,8 @@ def PNN(dnn_feature_columns, embedding_size=8, dnn_hidden_units=(128, 128), l2_r
 
     sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                          l2_reg_embedding, init_std, seed)
-    inner_product = tf.keras.layers.Flatten()(InnerProductLayer()(sparse_embedding_list))
+    inner_product = tf.keras.layers.Flatten()(
+        InnerProductLayer()(sparse_embedding_list))
     outter_product = OutterProductLayer(kernel_type)(sparse_embedding_list)
 
     # ipnn deep input
@@ -64,9 +65,9 @@ def PNN(dnn_feature_columns, embedding_size=8, dnn_hidden_units=(128, 128), l2_r
     else:
         deep_input = linear_signal
 
-    dnn_input = combined_dnn_input([deep_input],dense_value_list)
+    dnn_input = combined_dnn_input([deep_input], dense_value_list)
     dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                   False, seed)(dnn_input)
+                  False, seed)(dnn_input)
     dnn_logit = tf.keras.layers.Dense(
         1, use_bias=False, activation=None)(dnn_out)
 

deepctr/models/wdl.py

@@ -10,7 +10,7 @@
 from tensorflow.python.keras.models import Model
 from tensorflow.python.keras.layers import Dense
 
-from ..inputs import build_input_features, get_linear_logit,input_from_feature_columns,combined_dnn_input
+from ..inputs import build_input_features, get_linear_logit, input_from_feature_columns, combined_dnn_input
 from ..layers.core import PredictionLayer, DNN
 from ..layers.utils import add_func
 
@@ -34,7 +34,8 @@ def WDL(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128)
     :return: A Keras model instance.
     """
 
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 

deepctr/models/xdeepfm.py

@@ -8,10 +8,10 @@
 """
 import tensorflow as tf
 
-from ..inputs import input_from_feature_columns, get_linear_logit,build_input_features,combined_dnn_input
+from ..inputs import input_from_feature_columns, get_linear_logit, build_input_features, combined_dnn_input
 from ..layers.core import PredictionLayer, DNN
 from ..layers.interaction import CIN
-from ..layers.utils import concat_func,add_func
+from ..layers.utils import concat_func, add_func
 
 
 def xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 256),
@@ -39,8 +39,8 @@ def xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256,
     :return: A Keras model instance.
     """
 
-
-    features = build_input_features(linear_feature_columns + dnn_feature_columns)
+    features = build_input_features(
+        linear_feature_columns + dnn_feature_columns)
 
     inputs_list = list(features.values())
 
@@ -52,9 +52,9 @@ def xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256,
 
     fm_input = concat_func(sparse_embedding_list, axis=1)
 
-    dnn_input = combined_dnn_input(sparse_embedding_list,dense_value_list)
+    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
     dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                   dnn_use_bn, seed)(dnn_input)
+                     dnn_use_bn, seed)(dnn_input)
     dnn_logit = tf.keras.layers.Dense(
         1, use_bias=False, activation=None)(dnn_output)
 
@@ -64,9 +64,7 @@ def xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256,
         exFM_out = CIN(cin_layer_size, cin_activation,
                        cin_split_half, l2_reg_cin, seed)(fm_input)
         exFM_logit = tf.keras.layers.Dense(1, activation=None, )(exFM_out)
-        final_logit = add_func([final_logit,exFM_logit])
-
-
+        final_logit = add_func([final_logit, exFM_logit])
 
     output = PredictionLayer(task)(final_logit)