change fetch var

chenjiyan2001 · May 10, 2021 · c64fefc · c64fefc
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diff --git a/doc/inference.md b/doc/inference.md
@@ -12,8 +12,8 @@ runner:
   ...
   # use inference save model
   use_inference: True  # 静态图训练时保存为inference model
-  save_inference_feed_varnames: ["label","C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","dense_input"] # inference model 的feed参数的名字
-  save_inference_fetch_varnames: ["cast_0.tmp_0"] # inference model 的fetch参数的名字
+  save_inference_feed_varnames: ["C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","dense_input"] # inference model 的feed参数的名字
+  save_inference_fetch_varnames: ["sigmoid_0.tmp_0"] # inference model 的fetch参数的名字
 ```
 3. 启动静态图训练
 ```bash
@@ -54,7 +54,7 @@ python -u ../../../tools/to_static.py -m config.yaml
 # 在最后输出的list中，去除第一个np.array，即label部分。
   yield output_list[1:]
 ```
-将inference预测得到的prediction预测值和数据集中的label对比，使用另外的脚本计算auc即可得到和静态图相同的auc结果。
+将inference预测得到的prediction预测值和数据集中的label对比，使用另外的脚本计算auc指标即可。
 
 ## 将保存的模型使用Inference预测库进行服务端部署
 paddlerec提供tools/paddle_infer.py脚本，供您方便的使用inference预测库高效的对模型进行预测。  

diff --git a/models/rank/wide_deep/config.yaml b/models/rank/wide_deep/config.yaml
@@ -22,7 +22,7 @@ runner:
   train_batch_size: 2
   epochs: 3
   print_interval: 2
-  #model_init_path: "output_model_wide_deep/2" # init model
+  # model_init_path: "output_model_wide_deep/2" # init model
   model_save_path: "output_model_wide_deep"
   test_data_dir: "data/sample_data/train"
   infer_reader_path: "criteo_reader" # importlib format
@@ -32,8 +32,8 @@ runner:
   infer_end_epoch: 3
   #use inference save model
   use_inference: False
-  save_inference_feed_varnames: ["label","C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","dense_input"]
-  save_inference_fetch_varnames: ["cast_0.tmp_0"]
+  save_inference_feed_varnames: ["C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","dense_input"]
+  save_inference_fetch_varnames: ["sigmoid_0.tmp_0"]
 
 # hyper parameters of user-defined network
 hyper_parameters: