update doc

Author: 浅梦

README.md

@@ -24,7 +24,9 @@ DeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-lear
 - Provide  `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord)
 - It is compatible with both `tf 1.x`  and `tf 2.x`.
 
-
+Some related project:
+- DeepMatch: https://github.com/shenweichen/DeepMatch
+- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch
 
 
 Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955)) and [welcome to join us!](./CONTRIBUTING.md)
@@ -78,3 +80,13 @@ Please follow our wechat to join group:
 - wechat ID: **deepctrbot**
 
   ![wechat](./docs/pics/code.png)
+  
+##  Cooperative promotion 合作推广
+For more information about the recommendation system, such as **feature engineering, user profile, matching, ranking and multi-objective optimization, online learning and real-time computing, and more cutting-edge technologies and practical projects**:
+
+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)
+

deepctr/layers/utils.py

@@ -126,7 +126,7 @@ def compute_mask(self, inputs, mask):
         return None
 
     def get_config(self, ):
-        config = {'mode': self.mode, 'l2_reg': self.l2_reg, 'use_bias': self.use_bias}
+        config = {'mode': self.mode, 'l2_reg': self.l2_reg, 'use_bias': self.use_bias, 'seed': self.seed}
         base_config = super(Linear, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 

docs/requirements.readthedocs.txt

@@ -1 +1 @@
-tensorflow==1.12.0
+tensorflow==1.15.2

docs/source/index.rst

@@ -56,6 +56,18 @@ DisscussionGroup 交流群
 
 .. image:: ../pics/code.png
 
+Cooperative promotion 合作推广
+------------------------------
+
+For more information about the recommendation system, such as **feature engineering, user profile, matching, ranking and multi-objective optimization, online learning and real-time computing, and more cutting-edge technologies and practical projects** :
+
+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>`_
+
+
+
 .. toctree::
    :maxdepth: 2
    :caption: Home: