v0.8.2

Refactor DNN Layer

Author: 浅梦

.github/ISSUE_TEMPLATE/bug_report.md

@@ -19,8 +19,8 @@ Steps to reproduce the behavior:
 
 **Operating environment(运行环境):**
  - python version [e.g. 3.5, 3.7]
- - tensorflow version [e.g. 1.4.0, 1.15.0, 2.2.0]
- - deepctr version [e.g. 0.8.0,]
+ - tensorflow version [e.g. 1.4.0, 1.15.0, 2.3.0]
+ - deepctr version [e.g. 0.8.2,]
 
 **Additional context**
 Add any other context about the problem here.

.github/ISSUE_TEMPLATE/question.md

@@ -16,5 +16,5 @@ Add any other context about the problem here.
 
 **Operating environment(运行环境):**
  - python version [e.g. 3.6]
- - tensorflow version [e.g. 1.4.0, 1.5.0, 2.2.0]
- - deepctr version [e.g. 0.8.0,]
+ - tensorflow version [e.g. 1.4.0, 1.5.0, 2.3.0]
+ - deepctr version [e.g. 0.8.2,]

README.md

@@ -57,16 +57,16 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star
 
 ## Citation
 
-- Weichen Shen. (2018). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.
+- Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.
 
 
 If you find this code useful in your research, please cite it using the following BibTeX:
 
 ```bibtex
-@misc{shen2018deepctr,
+@misc{shen2017deepctr,
   author = {Weichen Shen},
   title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
-  year = {2018},
+  year = {2017},
   publisher = {GitHub},
   journal = {GitHub Repository},
   howpublished = {\url{https://github.com/shenweichen/deepctr}},
@@ -86,7 +86,6 @@ For more information about the recommendation system, such as **feature engineer
 
 更多关于推荐系统的内容，如**特征工程，用户画像，召回，排序和多目标优化，在线学习与实时计算以及更多前沿技术和实战项目**等可参考：
 
-
 - [推荐系统实战](https://www.julyedu.com/course/getDetail/181?ccode=5ee751d37278c)
-- [推荐系统就业小班](https://www.julyedu.com/course/getDetail/321?ccode=5ee751d37278c)
+- [互联网计算广告实战](https://www.julyedu.com/course/getDetail/158?ccode=5ee751d37278c)
 

deepctr/__init__.py

@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.8.1'
+__version__ = '0.8.2'
 check_version(__version__)

deepctr/estimator/models/autoint.py

@@ -73,14 +73,12 @@ def _model_fn(features, labels, mode, config):
             dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
 
             if len(dnn_hidden_units) > 0 and att_layer_num > 0:  # Deep & Interacting Layer
-                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                               dnn_use_bn, seed)(dnn_input, training=train_flag)
+                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input, training=train_flag)
                 stack_out = tf.keras.layers.Concatenate()([att_output, deep_out])
                 final_logit = tf.keras.layers.Dense(
                     1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(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, training=train_flag)
+                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input, training=train_flag)
                 final_logit = tf.keras.layers.Dense(
                     1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out)
             elif att_layer_num > 0:  # Only Interacting Layer

deepctr/estimator/models/ccpm.py

@@ -80,8 +80,7 @@ def _model_fn(features, labels, mode, config):
                     k=min(k, int(conv_result.shape[1])), axis=1)(conv_result)
 
             flatten_result = tf.keras.layers.Flatten()(pooling_result)
-            dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn,
-                          dropout_rate=dnn_dropout, seed=seed)(flatten_result, training=train_flag)
+            dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, seed=seed)(flatten_result, training=train_flag)
             dnn_logit = tf.keras.layers.Dense(1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_out)
 
         logits = linear_logits + dnn_logit

deepctr/estimator/models/dcn.py

@@ -63,15 +63,13 @@ def _model_fn(features, labels, mode, config):
             dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
 
             if len(dnn_hidden_units) > 0 and cross_num > 0:  # Deep & Cross
-                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                               dnn_use_bn, seed)(dnn_input, training=train_flag)
+                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input, training=train_flag)
                 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, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(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, training=train_flag)
+                deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input, training=train_flag)
                 final_logit = tf.keras.layers.Dense(
                     1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out)
             elif cross_num > 0:  # Only Cross

deepctr/estimator/models/deepfm.py

@@ -63,8 +63,7 @@ def _model_fn(features, labels, mode, config):
 
             fm_logit = FM()(concat_func(sparse_embedding_list, axis=1))
 
-            dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                             dnn_use_bn, seed)(dnn_input, training=train_flag)
+            dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input, training=train_flag)
             dnn_logit = tf.keras.layers.Dense(
                 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(dnn_output)
 

deepctr/estimator/models/fibinet.py

@@ -68,8 +68,7 @@ def _model_fn(features, labels, mode, config):
 
             dnn_input = combined_dnn_input(
                 [Flatten()(concat_func([senet_bilinear_out, bilinear_out]))], dense_value_list)
-            dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                          False, seed)(dnn_input, training=train_flag)
+            dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input, training=train_flag)
             dnn_logit = Dense(
                 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_out)
 

deepctr/estimator/models/fnn.py

@@ -53,8 +53,7 @@ def _model_fn(features, labels, mode, config):
             sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                                  l2_reg_embedding=l2_reg_embedding)
             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, training=train_flag)
+            deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input, training=train_flag)
             dnn_logit = tf.keras.layers.Dense(
                 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out)