From dd7dcd6b54276deeb847a0b39b853a994f45dfe6 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 6 Mar 2017 19:26:04 +0800
Subject: [PATCH] Rename reader_dict

---
 label_semantic_roles/README.en.md | 8 ++++----
 label_semantic_roles/README.md    | 6 +++---
 label_semantic_roles/train.py     | 4 ++--
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/label_semantic_roles/README.en.md b/label_semantic_roles/README.en.md
index 4accfe0..b2a75cb 100644
--- a/label_semantic_roles/README.en.md
+++ b/label_semantic_roles/README.en.md
@@ -440,15 +440,15 @@ trainer = paddle.trainer.SGD(cost=crf_cost,
 As mentioned in data preparation section, we will use CoNLL 2005 test corpus as training data set. `conll05.test()` outputs one training instance at a time. It will be shuffled, and batched into mini batches as input.
 
 ```python
-reader = paddle.reader.batched(
+reader = paddle.batch(
     paddle.reader.shuffle(
         conll05.test(), buf_size=8192), batch_size=20)
 ```
 
-`reader_dict` is used to specify relationship between data instance and layer layer. For example, according to following `reader_dict`, the 0th column of data instance produced by`conll05.test()` correspond to data layer named `word_data`.
+`feeding` is used to specify relationship between data instance and layer layer. For example, according to following `feeding`, the 0th column of data instance produced by`conll05.test()` correspond to data layer named `word_data`.
 
 ```python
-reader_dict = {
+feeding = {
     'word_data': 0,
     'ctx_n2_data': 1,
     'ctx_n1_data': 2,
@@ -478,7 +478,7 @@ trainer.train(
     reader=reader,
     event_handler=event_handler,
     num_passes=10000,
-    reader_dict=reader_dict)
+    feeding=feeding)
 ```
 
 ## Conclusion
diff --git a/label_semantic_roles/README.md b/label_semantic_roles/README.md
index a96d960..0bead42 100644
--- a/label_semantic_roles/README.md
+++ b/label_semantic_roles/README.md
@@ -409,11 +409,11 @@ reader = paddle.batch(
         conll05.test(), buf_size=8192), batch_size=20)
 ```
 
-通过`reader_dict`来指定每一个数据和data_layer的对应关系。 例如 下面`reader_dict`表示: `conll05.test()`产生数据的第0列对应`word_data`层的特征。
+通过`feeding`来指定每一个数据和data_layer的对应关系。 例如 下面`feeding`表示: `conll05.test()`产生数据的第0列对应`word_data`层的特征。
 
 
 ```python
-reader_dict = {
+feeding = {
     'word_data': 0,
     'ctx_n2_data': 1,
     'ctx_n1_data': 2,
@@ -443,7 +443,7 @@ trainer.train(
     reader=reader,
     event_handler=event_handler,
     num_passes=10000,
-    reader_dict=reader_dict)
+    feeding=feeding)
 ```
 
 ## 总结
diff --git a/label_semantic_roles/train.py b/label_semantic_roles/train.py
index f88a709..623b614 100644
--- a/label_semantic_roles/train.py
+++ b/label_semantic_roles/train.py
@@ -159,7 +159,7 @@ def main():
         paddle.reader.shuffle(
             conll05.test(), buf_size=8192), batch_size=10)
 
-    reader_dict = {
+    feeding = {
         'word_data': 0,
         'ctx_n2_data': 1,
         'ctx_n1_data': 2,
@@ -181,7 +181,7 @@ def main():
         reader=reader,
         event_handler=event_handler,
         num_passes=10000,
-        reader_dict=reader_dict)
+        feeding=feeding)
 
 
 if __name__ == '__main__':
-- 
GitLab