update w2v

models/recall/word2vec/README.md

@@ -38,11 +38,15 @@
 - [FAQ](#FAQ)
 
 ## 模型简介
-本例实现了skip-gram模式的word2vector模型，如下图中的skip-gram部分：
-![](https://ai-studio-static-online.cdn.bcebos.com/bf0217bcb42e455284290a100670072989d432939b7e43e38b78eea6b60732c0)  
+本例实现了skip-gram模式的word2vector模型，如下图所示：
+<p align="center">
+<img align="center" src="../../../doc/imgs/word2vec.png">
+<p>
 以每一个词为中心词X，然后在窗口内和临近的词Y组成样本对（X,Y）用于网络训练。在实际训练过程中还会根据自定义的负采样率生成负样本来加强训练的效果  
 具体的训练思路如下：  
-![](https://ai-studio-static-online.cdn.bcebos.com/ad45d6dfff2f4fa69c639a6b5d2bbe46c79ac67658464aa0a069a04eb2800cb6)
+<p align="center">
+<img align="center" src="../../../doc/imgs/w2v_train.png">
+<p>
 
 推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/124377)教程获取更详细的信息。
 

models/recall/word2vec/config.yaml

@@ -42,38 +42,40 @@ hyper_parameters:
   window_size: 5
 
 # select runner by name
-mode: train_runner
+mode: [single_cpu_train, single_cpu_infer]
 # config of each runner.
 # runner is a kind of paddle training class, which wraps the train/infer process.
 runner:
-- name: train_runner
+- name: single_cpu_train
   class: train
   # num of epochs
-  epochs: 2
+  epochs: 5
   # device to run training or infer
   device: cpu
   save_checkpoint_interval: 1 # save model interval of epochs
   save_inference_interval: 1 # save inference
-  save_checkpoint_path: "increment" # save checkpoint path
-  save_inference_path: "inference" # save inference path
+  save_checkpoint_path: "increment_w2v" # save checkpoint path
+  save_inference_path: "inference_w2v" # save inference path
   save_inference_feed_varnames: [] # feed vars of save inference
   save_inference_fetch_varnames: [] # fetch vars of save inference
   init_model_path: "" # load model path
   print_interval: 1
-- name: infer_runner
+  phases: [phase1]
+- name: single_cpu_infer
   class: infer
   # device to run training or infer
   device: cpu
-  init_model_path: "increment/0" # load model path
+  init_model_path: "increment_w2v" # load model path
   print_interval: 1
+  phases: [phase2]
 
 # runner will run all the phase in each epoch
 phase:
 - name: phase1
   model: "{workspace}/model.py" # user-defined model
   dataset_name: dataset_train # select dataset by name
+  thread_num: 5
+- name: phase2
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataset_infer # select dataset by name
   thread_num: 1
-# - name: phase2
-#   model: "{workspace}/model.py" # user-defined model
-#   dataset_name: dataset_infer # select dataset by name
-#   thread_num: 1