Merge pull request #450 from reyoung/feature/pre-commit-hooks-scripts

Feature/pre commit hooks scripts

.clang-format

@@ -13,8 +13,6 @@
 # The document of clang-format is 
 #   http://clang.llvm.org/docs/ClangFormat.html
 #   http://clang.llvm.org/docs/ClangFormatStyleOptions.html
-#
-# TODO(yuyang18): Add python and other language code style
 ---
 Language:        Cpp
 BasedOnStyle:  Google
@@ -22,8 +20,9 @@ IndentWidth:     2
 TabWidth:        2
 ContinuationIndentWidth: 4
 AccessModifierOffset: -2  # The private/protected/public has no indent in class
-PointerAlignment: Left    # int* p/int& p, not int *p/int &p
 Standard:  Cpp11 
 AllowAllParametersOfDeclarationOnNextLine: true
+BinPackParameters: false
+BinPackArguments: false
 ...
 

.pre-commit-config.yaml

+-   repo: https://github.com/Lucas-C/pre-commit-hooks.git
+    sha: c25201a00e6b0514370501050cf2a8538ac12270
+    hooks:
+    -   id: remove-crlf
+-   repo: https://github.com/reyoung/mirrors-yapf.git
+    sha: v0.13.2
+    hooks:
+    -   id: yapf
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    sha: 4ef03c4223ad322c7adaa6c6c0efb26b57df3b71
+    hooks:
+    -   id: check-added-large-files
+    -   id: check-merge-conflict
+    -   id: check-symlinks
+    -   id: detect-private-key
+    -   id: end-of-file-fixer
+# TODO(yuyang): trailing whitespace has some bugs on markdown 
+# files now, please not add it to pre-commit hook now
+#    -   id: trailing-whitespace
+#
+# TODO(yuyang): debug-statements not fit for Paddle, because
+# not all of our python code is runnable. Some are used for 
+# documenation
+#    -   id: debug-statements

demo/introduction/README.md

 This folder contains scripts used in PaddlePaddle introduction.
 - use `bash train.sh` to train a simple linear regression model
 - use `python evaluate_model.py` to read model parameters. You can see that `w` and `b` are very close to [2, 0.3].
-

demo/mnist/data/get_mnist_data.sh

@@ -19,4 +19,3 @@ done
 cd $DIR
 rm -f *.list
 python generate_list.py
-

demo/recommendation/data/config.json

@@ -14,4 +14,3 @@
     "fields": ["id", "title", "genres"]
   }
 }
-

demo/semantic_role_labeling/test.sh

@@ -37,4 +37,3 @@ paddle train \
   --use_gpu=false \
   --config_args=is_test=1 \
 2>&1 | tee 'test.log'
-

demo/semantic_role_labeling/train.sh

@@ -24,4 +24,3 @@ paddle train \
   --show_parameter_stats_period=10 \
   --test_all_data_in_one_period=1 \
 2>&1 | tee 'train.log'
-

doc/demo/semantic_role_labeling/semantic_role_labeling.md

-# Semantic Role labeling Tutorial #
-
-Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering.  An instance is as following [1]:
-
- [ <sub>A0</sub> He ] [ <sub>AM-MOD</sub> would ][ <sub>AM-NEG</sub> n’t ] [ <sub>V</sub> accept] [ <sub>A1</sub> anything of value ] from [<sub>A2</sub> those he was writing about ]. 
-
-- V: verb
-- A0: acceptor
-- A1: thing accepted
-- A2: accepted-from
-- A3: Attribute
-- AM-MOD: modal 
-- AM-NEG: negation
-
-Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. 
-
-To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. 
-
-## Data Description
-The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license,  the demo adopts the test data set of CoNLL-2005, which can be reached on website.
-
-To download and process the original data, user just need to execute the following command:
-
-```bash
-cd data
-./get_data.sh
-```
-Several new files appear in the `data `directory as follows.
-```bash
-conll05st-release：the test data set of CoNll-2005 shared task 
-test.wsj.words：the Wall Street Journal data sentences
-test.wsj.props:  the propositional arguments
-src.dict：the dictionary of words in sentences
-tgt.dict：the labels dictionary
-feature: the extracted features from data set
-```
-
-## Training
-### DB-LSTM
-Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. 
-
-Unlike Bidirectional-LSTM that used in Sentiment Analysis demo,  the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. 
-
-The following figure shows a temporal expanded 2-layer DB-LSTM network.
-<center>
-![pic](./network_arch.png)
-</center>
-
-### Features
-Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark m<sub>r</sub> = 1 to denote the argument position if it locates in the predicate context region, or m<sub>r</sub> = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]:
-<center>
-![pic](./feature.jpg)
-</center>
-
-In this sample, the coresponding labelled sentence is:
-
-[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] . 
-
-In the demo, we adopt the feature template as above, consists of :  `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`.
-
-### Data Provider
-
-`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The  Six features and label are all IndexSlots.
-```
-def hook(settings, word_dict, label_dict, **kwargs):
-    settings.word_dict = word_dict
-    settings.label_dict = label_dict
-    #all inputs are integral and sequential type
-    settings.slots = [
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(2),
-        integer_value_sequence(len(label_dict))]
-```
-The corresponding data iterator is as following:
-```
-@provider(use_seq=True, init_hook=hook)
-def process(obj, file_name):
-    with open(file_name, 'r') as fdata:
-        for line in fdata:
-            sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t')
-            words = sentence.split()
-            sen_len = len(words)
-            word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
-
-            predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
-            ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len
-            ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len
-            ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len
-
-            marks = mark.split()
-            mark_slot = [int(w) for w in marks]
-
-            label_list = label.split()
-            label_slot = [obj.label_dict.get(w) for w in label_list]
-
-            yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
-```
-The `process`function yield 7 lists which are six features and labels.
- 
-### Neural Network Config
-`db_lstm.py` is the neural network config file to load the dictionaries and define the  data provider module and network architecture during the training procedure. 
-
-Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` .  Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
-
-### Run Training 
-The script for training is `train.sh`, user just need to execute:
-```bash
-  ./train.sh
-```
-The content in `train.sh`:
-```
-paddle train \
-  --config=./db_lstm.py \
-  --save_dir=./output \
-  --trainer_count=4 \
-  --log_period=10 \
-  --num_passes=500 \
-  --use_gpu=false \
-  --show_parameter_stats_period=10 \
-  --test_all_data_in_one_period=1 \
-2>&1 | tee 'train.log'
-```
-
--  \--config=./db_lstm.py : network config file.
--  \--save_di=./output: output path to save models.
--  \--trainer_count=4 : set thread number (or GPU count).
--  \--log_period=10 : print log every 20 batches.
--  \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
--  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
--  \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
--  \--test_all_data_in_one_period=1: test all data in every testing.
-
-
-After training, the models  will be saved in directory `output`.
-
-### Run testing
-The script for testing is `test.sh`, user just need to execute:
-```bash
-  ./test.sh
-```
-The main part in `tesh.sh`
-```
-paddle train \
-  --config=./db_lstm.py \
-  --model_list=$model_list \
-  --job=test \
-  --config_args=is_test=1 \
-```
-
-  - \--config=./db_lstm.py: network config file
-  - \--model_list=$model_list.list: model list file
-  - \--job=test: indicate the test job
-  - \--config_args=is_test=1: flag to indicate test
-  
-
-### Run prediction
-The script for prediction is `predict.sh`, user just need to execute:
-```bash
-  ./predict.sh
-  
-```
-In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
-```
-python predict.py 
-     -c $config_file 
-     -w $model_path 
-     -l $label_file 
-     -d $dict_file 
-     -i $input_file
-```
-
-`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
-
-After prediction,  the result is saved in `predict.res`.
-
-## Reference
-[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. 
-
-[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
+# Semantic Role labeling Tutorial #
+
+Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering.  An instance is as following [1]:
+
+ [ <sub>A0</sub> He ] [ <sub>AM-MOD</sub> would ][ <sub>AM-NEG</sub> n’t ] [ <sub>V</sub> accept] [ <sub>A1</sub> anything of value ] from [<sub>A2</sub> those he was writing about ]. 
+
+- V: verb
+- A0: acceptor
+- A1: thing accepted
+- A2: accepted-from
+- A3: Attribute
+- AM-MOD: modal 
+- AM-NEG: negation
+
+Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. 
+
+To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. 
+
+## Data Description
+The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license,  the demo adopts the test data set of CoNLL-2005, which can be reached on website.
+
+To download and process the original data, user just need to execute the following command:
+
+```bash
+cd data
+./get_data.sh
+```
+Several new files appear in the `data `directory as follows.
+```bash
+conll05st-release：the test data set of CoNll-2005 shared task 
+test.wsj.words：the Wall Street Journal data sentences
+test.wsj.props:  the propositional arguments
+src.dict：the dictionary of words in sentences
+tgt.dict：the labels dictionary
+feature: the extracted features from data set
+```
+
+## Training
+### DB-LSTM
+Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. 
+
+Unlike Bidirectional-LSTM that used in Sentiment Analysis demo,  the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. 
+
+The following figure shows a temporal expanded 2-layer DB-LSTM network.
+<center>
+![pic](./network_arch.png)
+</center>
+
+### Features
+Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark m<sub>r</sub> = 1 to denote the argument position if it locates in the predicate context region, or m<sub>r</sub> = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]:
+<center>
+![pic](./feature.jpg)
+</center>
+
+In this sample, the coresponding labelled sentence is:
+
+[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] . 
+
+In the demo, we adopt the feature template as above, consists of :  `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`.
+
+### Data Provider
+
+`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The  Six features and label are all IndexSlots.
+```
+def hook(settings, word_dict, label_dict, **kwargs):
+    settings.word_dict = word_dict
+    settings.label_dict = label_dict
+    #all inputs are integral and sequential type
+    settings.slots = [
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(2),
+        integer_value_sequence(len(label_dict))]
+```
+The corresponding data iterator is as following:
+```
+@provider(use_seq=True, init_hook=hook)
+def process(obj, file_name):
+    with open(file_name, 'r') as fdata:
+        for line in fdata:
+            sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t')
+            words = sentence.split()
+            sen_len = len(words)
+            word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
+
+            predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
+            ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len
+            ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len
+            ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len
+
+            marks = mark.split()
+            mark_slot = [int(w) for w in marks]
+
+            label_list = label.split()
+            label_slot = [obj.label_dict.get(w) for w in label_list]
+
+            yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
+```
+The `process`function yield 7 lists which are six features and labels.
+ 
+### Neural Network Config
+`db_lstm.py` is the neural network config file to load the dictionaries and define the  data provider module and network architecture during the training procedure. 
+
+Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` .  Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
+
+### Run Training 
+The script for training is `train.sh`, user just need to execute:
+```bash
+  ./train.sh
+```
+The content in `train.sh`:
+```
+paddle train \
+  --config=./db_lstm.py \
+  --save_dir=./output \
+  --trainer_count=4 \
+  --log_period=10 \
+  --num_passes=500 \
+  --use_gpu=false \
+  --show_parameter_stats_period=10 \
+  --test_all_data_in_one_period=1 \
+2>&1 | tee 'train.log'
+```
+
+-  \--config=./db_lstm.py : network config file.
+-  \--save_di=./output: output path to save models.
+-  \--trainer_count=4 : set thread number (or GPU count).
+-  \--log_period=10 : print log every 20 batches.
+-  \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
+-  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
+-  \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
+-  \--test_all_data_in_one_period=1: test all data in every testing.
+
+
+After training, the models  will be saved in directory `output`.
+
+### Run testing
+The script for testing is `test.sh`, user just need to execute:
+```bash
+  ./test.sh
+```
+The main part in `tesh.sh`
+```
+paddle train \
+  --config=./db_lstm.py \
+  --model_list=$model_list \
+  --job=test \
+  --config_args=is_test=1 \
+```
+
+  - \--config=./db_lstm.py: network config file
+  - \--model_list=$model_list.list: model list file
+  - \--job=test: indicate the test job
+  - \--config_args=is_test=1: flag to indicate test
+  
+
+### Run prediction
+The script for prediction is `predict.sh`, user just need to execute:
+```bash
+  ./predict.sh
+  
+```
+In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
+```
+python predict.py 
+     -c $config_file 
+     -w $model_path 
+     -l $label_file 
+     -d $dict_file 
+     -i $input_file
+```
+
+`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
+
+After prediction,  the result is saved in `predict.res`.
+
+## Reference
+[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. 
+
+[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.

doc/introduction/index.md

@@ -98,4 +98,3 @@ There, you have recovered the underlying pattern between `X` and `Y` only from o
 - <a href="../build/index.html"> Build and Installation </a>
 - <a href="../demo/quick_start/index_en.html">Quick Start</a>
 - <a href="../demo/index.html">Example and Demo</a>
-

doc_cn/algorithm/rnn/hierarchical-layer.md

-# 支持双层序列作为输入的Layer
-
-## 概述
-
-在自然语言处理任务中，序列是一种常见的数据类型。一个独立的词语，可以看作是一个非序列输入，或者，我们称之为一个0层的序列；由词语构成的句子，是一个单层序列；若干个句子构成一个段落，是一个双层的序列。
-
-双层序列是一个嵌套的序列，它的每一个元素，又是一个单层的序列。这是一种非常灵活的数据组织方式，帮助我们构造一些复杂的输入信息。
-
-我们可以按照如下层次定义非序列，单层序列，以及双层序列。
-
-+ 0层序列：一个独立的元素，类型可以是PaddlePaddle支持的任意输入数据类型
-+ 单层序列：排成一列的多个元素，每个元素是一个0层序列，元素之间的顺序是重要的输入信息
-+ 双层序列：排成一列的多个元素，每个元素是一个单层序列，称之为双层序列的一个子序列（subseq），subseq的每个元素是一个0层序列
-
-
-在 PaddlePaddle中，下面这些Layer能够接受双层序列作为输入，完成相应的计算。
-## pooling_layer
-
-pooling_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#pooling-layer">配置API</a>。
-```python
-seq_pool = pooling_layer(input=layer,
-                         pooling_type=AvgPooling(),
-                         agg_level=AggregateLevel.EACH_SEQUENCE)
-```
-- `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。
-- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
-  - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列
-  - 输入：一个双层序列，或一个单层序列
-  - 输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值）
-- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
-  - 作用：一个双层序列经过运算变成一个单层序列
-  - 输入：必须是一个双层序列
-  - 输出：一个单层序列，序列的每个元素是原来双层序列每个subseq元素的平均值（或最大值）
-
-## last_seq 和 first_seq
-
-last_seq的使用示例如下（first_seq类似），详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#last-seq">配置API</a>。
-```python
-last = last_seq(input=layer,
-                agg_level=AggregateLevel.EACH_SEQUENCE)
-```
-- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
-  - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列
-  - 输入：一个双层序列或一个单层序列
-  - 输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。
-- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
-  - 作用：一个双层序列经过运算变成一个单层序列
-  - 输入：必须是一个双层序列
-  - 输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。
-
-## expand_layer
-
-expand_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#expand-layer">配置API</a>。
-```python
-expand = expand_layer(input=layer1,
-                      expand_as=layer2,
-                      expand_level=ExpandLevel.FROM_TIMESTEP)
-```
-- `expand_level=ExpandLevel.FROM_TIMESTEP`时（默认值）：
-  - 作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列
-  - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息
-  - 输出：一个单层序列，或一个双层序列，输出序列的类型（双层序列，或单层序列）和序列中含有元素的数目同 layer2一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝
-- `expand_level=ExpandLevel.FROM_SEQUENCE`时：
-  - 作用：一个单层序列经过运算扩展成一个双层序列
-  - 输入：layer1必须是一个单层序列，是待扩展的数据；layer2必须是一个双层序列，提供扩展的长度信息
-  - 输出：一个双层序列，序列中含有元素的数目同layer2一致。要求单层序列含有元素的数目（0层序列），和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个subseq。
\ No newline at end of file
+# 支持双层序列作为输入的Layer
+
+## 概述
+
+在自然语言处理任务中，序列是一种常见的数据类型。一个独立的词语，可以看作是一个非序列输入，或者，我们称之为一个0层的序列；由词语构成的句子，是一个单层序列；若干个句子构成一个段落，是一个双层的序列。
+
+双层序列是一个嵌套的序列，它的每一个元素，又是一个单层的序列。这是一种非常灵活的数据组织方式，帮助我们构造一些复杂的输入信息。
+
+我们可以按照如下层次定义非序列，单层序列，以及双层序列。
+
++ 0层序列：一个独立的元素，类型可以是PaddlePaddle支持的任意输入数据类型
++ 单层序列：排成一列的多个元素，每个元素是一个0层序列，元素之间的顺序是重要的输入信息
++ 双层序列：排成一列的多个元素，每个元素是一个单层序列，称之为双层序列的一个子序列（subseq），subseq的每个元素是一个0层序列
+
+
+在 PaddlePaddle中，下面这些Layer能够接受双层序列作为输入，完成相应的计算。
+## pooling_layer
+
+pooling_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#pooling-layer">配置API</a>。
+```python
+seq_pool = pooling_layer(input=layer,
+                         pooling_type=AvgPooling(),
+                         agg_level=AggregateLevel.EACH_SEQUENCE)
+```
+- `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。
+- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
+  - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列
+  - 输入：一个双层序列，或一个单层序列
+  - 输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值）
+- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
+  - 作用：一个双层序列经过运算变成一个单层序列
+  - 输入：必须是一个双层序列
+  - 输出：一个单层序列，序列的每个元素是原来双层序列每个subseq元素的平均值（或最大值）
+
+## last_seq 和 first_seq
+
+last_seq的使用示例如下（first_seq类似），详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#last-seq">配置API</a>。
+```python
+last = last_seq(input=layer,
+                agg_level=AggregateLevel.EACH_SEQUENCE)
+```
+- `agg_level=AggregateLevel.TIMESTEP`时（默认值）：
+  - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列
+  - 输入：一个双层序列或一个单层序列
+  - 输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。
+- `agg_level=AggregateLevel.EACH_SEQUENCE`时：
+  - 作用：一个双层序列经过运算变成一个单层序列
+  - 输入：必须是一个双层序列
+  - 输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。
+
+## expand_layer
+
+expand_layer的使用示例如下，详细见<a href = "../../../doc/ui/api/trainer_config_helpers/layers.html#expand-layer">配置API</a>。
+```python
+expand = expand_layer(input=layer1,
+                      expand_as=layer2,
+                      expand_level=ExpandLevel.FROM_TIMESTEP)
+```
+- `expand_level=ExpandLevel.FROM_TIMESTEP`时（默认值）：
+  - 作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列
+  - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息
+  - 输出：一个单层序列，或一个双层序列，输出序列的类型（双层序列，或单层序列）和序列中含有元素的数目同 layer2一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝
+- `expand_level=ExpandLevel.FROM_SEQUENCE`时：
+  - 作用：一个单层序列经过运算扩展成一个双层序列
+  - 输入：layer1必须是一个单层序列，是待扩展的数据；layer2必须是一个双层序列，提供扩展的长度信息
+  - 输出：一个双层序列，序列中含有元素的数目同layer2一致。要求单层序列含有元素的数目（0层序列），和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个subseq。

doc_cn/algorithm/rnn/rnn-tutorial.md

@@ -93,4 +93,4 @@ memory只能在`recurrent_group`中定义和使用。memory不能独立存在，
 使用`beam_search`需要遵循以下约定：
 
 - 单层RNN：从一个word生成下一个word。
-- 双层RNN：即把单层RNN生成后的subseq给拼接成一个新的双层seq。从语义上看，也不存在一个subseq直接生成下一个subseq的情况。
\ No newline at end of file
+- 双层RNN：即把单层RNN生成后的subseq给拼接成一个新的双层seq。从语义上看，也不存在一个subseq直接生成下一个subseq的情况。

doc_cn/build_and_install/install/paddle_version.txt

@@ -8,4 +8,4 @@ PaddlePaddle 0.8.0b1, compiled with
     with_gflags: ON
     with_metric_learning:
     with_timer: OFF
-    with_predict_sdk:
\ No newline at end of file
+    with_predict_sdk:

doc_cn/faq/reduce_min_pool_size.py

@@ -3,4 +3,4 @@ def process(settings, filename):
     os.system('shuf %s > %s.shuf' % (filename, filename))  # shuffle before.
     with open('%s.shuf' % filename, 'r') as f:
         for line in f:
-            yield get_sample_from_line(line)
\ No newline at end of file
+            yield get_sample_from_line(line)

paddle/.common_test_util.sh

@@ -117,4 +117,4 @@ set_port()
         fi
     done
 
-}
\ No newline at end of file
+}

paddle/CMakeLists.txt

@@ -17,5 +17,3 @@ endif()
 if(WITH_SWIG_PY)
   add_subdirectory(api)
 endif()
-
-

paddle/api/PaddleAPIPrivate.h

@@ -65,4 +65,3 @@ struct ArgumentsPrivate {
     return *(std::shared_ptr<T>*)(rawPtr);
   }
 };
-

paddle/api/test/CMakeLists.txt

 add_test(NAME test_swig_api
-    COMMAND bash ${PROJ_ROOT}/paddle/api/test/run_tests.sh)
\ No newline at end of file
+    COMMAND bash ${PROJ_ROOT}/paddle/api/test/run_tests.sh)

paddle/api/test/testMatrix.py

@@ -69,8 +69,8 @@ class TestMatrix(unittest.TestCase):
     def test_numpy(self):
         numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
         m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat)
-        self.assertEqual(
-            (int(m.getHeight()), int(m.getWidth())), numpy_mat.shape)
+        self.assertEqual((int(m.getHeight()), int(m.getWidth())),
+                         numpy_mat.shape)
 
         # the numpy matrix and paddle matrix shared the same memory.
         numpy_mat[0, 1] = 342.23

paddle/cuda/include/hl_base.h

@@ -254,4 +254,3 @@ extern __thread cudaStream_t default_stream;
 #endif  /* __NVCC__ */
 
 #endif  /* HL_BASE_H_ */
-

paddle/cuda/include/stub/hl_cuda_cudnn_stub.h

@@ -199,4 +199,3 @@ inline void hl_batch_norm_backward(hl_tensor_descriptor inputDesc,
                                    real *savedInvVar) {}
 
 #endif  // HL_CUDA_CUDNN_STUB_H_
-

paddle/cuda/src/avx_mathfun.h

@@ -718,4 +718,3 @@ void sincos256_ps(v8sf x, v8sf *s, v8sf *c) {
   *s = _mm256_xor_ps(xmm1, sign_bit_sin);
   *c = _mm256_xor_ps(xmm2, sign_bit_cos);
 }
-

paddle/gserver/layers/FullyConnectedLayer.h

@@ -48,4 +48,3 @@ public:
 };
 
 }  // namespace paddle
-

paddle/math/MathFunctions.h

@@ -80,4 +80,3 @@ void vTanh(const int n, const T* a, T* r);
 }  // namespace paddle
 
 #endif  // MATHFUNCTIONS_H_
-

paddle/parameter/CMakeLists.txt

@@ -10,4 +10,4 @@ add_style_check_target(paddle_parameter ${PARAMETERS_HEADERS})
 add_dependencies(paddle_parameter gen_proto_cpp)
 if(WITH_TESTING)
     add_subdirectory(tests)
-endif()
\ No newline at end of file
+endif()

paddle/parameter/tests/CMakeLists.txt

-add_simple_unittest(test_common)
\ No newline at end of file
+add_simple_unittest(test_common)

paddle/scripts/CMakeLists.txt

@@ -6,4 +6,4 @@ configure_file(submit_local.sh.in
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/submit_local.sh DESTINATION bin
         PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ
             GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ
-        RENAME paddle)
\ No newline at end of file
+        RENAME paddle)

paddle/scripts/deb/build_scripts/build.sh

@@ -33,5 +33,3 @@ cmake .. -DWITH_GPU=ON -DWITH_SWIG_PY=ON -DWITH_AVX=OFF -DCUDNN_ROOT=/usr/
 make -j `nproc`
 cpack -D CPACK_GENERATOR='DEB' ..
 mv *.deb ~/dist/gpu-noavx
-
-

paddle/scripts/docker/generate.sh

@@ -58,4 +58,3 @@ m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \
    -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \
    -DPADDLE_WITH_AVX=OFF \
    Dockerfile.m4 > Dockerfile.gpu-noavx-demo
-

paddle/scripts/travis/common.sh

@@ -2,4 +2,3 @@
 set -e
 mkdir -p ../../../build
 cd ../../../build
-

paddle/trainer/tests/test.txt

@@ -998,4 +998,3 @@ from IN B-PP
 Friday NNP B-NP
 's POS B-NP
 Tokyo NNP I-NP
-

paddle/trainer/tests/test_gen_dict.txt

@@ -6,4 +6,4 @@
 5
 6
 7
-8
\ No newline at end of file
+8

paddle/trainer/tests/train.txt

@@ -4998,4 +4998,3 @@ However RB B-ADVP
 the DT B-NP
 disclosure NN I-NP
 of IN B-PP
-

paddle/utils/tests/test_CommandLineParser.cpp

@@ -109,4 +109,3 @@ int main(int argc, char** argv) {
 }
 
 #endif
-

python/paddle/trainer/config_parser.py

@@ -410,8 +410,8 @@ def RecurrentLayerGroupEnd(name):
                   "RecurrentLayerGroup not begin")
     for pair in g_current_submodel.memories:  #check exist
         layer = g_layer_map[pair.layer_name]
-        config_assert(layer is not None, "memory declare wrong name:%s" %
-                      pair.layer_name)
+        config_assert(layer is not None,
+                      "memory declare wrong name:%s" % pair.layer_name)
         memory_link = g_layer_map[pair.link_name]
         config_assert(layer.size == memory_link.size,
                       "memory declare wrong size:%d" % memory_link.size)
@@ -686,8 +686,8 @@ class ConvProjection(Projection):
         parse_conv(conv_conf, input_layer_name, self.proj_conf.conv_conf,
                    num_filters)
         # TODO: support rectangle input
-        self.proj_conf.output_size = (self.proj_conf.conv_conf.output_x**
-                                      2) * num_filters
+        self.proj_conf.output_size = (self.proj_conf.conv_conf.output_x
+                                      **2) * num_filters
 
     def calc_output_size(self, input_layer_config):
         return self.proj_conf.output_size
@@ -2793,8 +2793,8 @@ class ConcatenateLayer2(LayerBase):
 @config_layer('recurrent')
 class RecurrentLayer(LayerBase):
     def __init__(self, name, inputs, reversed=False, bias=True, **xargs):
-        super(RecurrentLayer, self).__init__(name, 'recurrent', 0, inputs, **
-                                             xargs)
+        super(RecurrentLayer, self).__init__(name, 'recurrent', 0, inputs,
+                                             **xargs)
         config_assert(len(self.inputs) == 1, 'RecurrentLayer must have 1 input')
         input_layer = self.get_input_layer(0)
         size = input_layer.size
@@ -2876,22 +2876,22 @@ class MDLstmLayer(LayerBase):
                  active_state_type="sigmoid",
                  bias=True,
                  **xargs):
-        super(MDLstmLayer, self).__init__(name, 'mdlstmemory', 0, inputs, **
-                                          xargs)
+        super(MDLstmLayer, self).__init__(name, 'mdlstmemory', 0, inputs,
+                                          **xargs)
         config_assert(len(self.inputs) == 1, 'MDLstmLayer must have 1 input')
         input_layer = self.get_input_layer(0)
         dim_num = len(directions)
         #check input_layer.size is divided by (3+dim_num)
-        config_assert(input_layer.size %
-                      (3 + dim_num) == 0, "size % (dim_num) should be 0!")
+        config_assert(input_layer.size % (3 + dim_num) == 0,
+                      "size % (dim_num) should be 0!")
         size = input_layer.size / (3 + dim_num)
         self.set_layer_size(size)
         self.config.active_gate_type = active_gate_type
         self.config.active_state_type = active_state_type
         for i in xrange(len(directions)):
             self.config.directions.append(int(directions[i]))
-        self.create_input_parameter(0, size * size *
-                                    (3 + dim_num), [size, size, 3 + dim_num])
+        self.create_input_parameter(0, size * size * (3 + dim_num),
+                                    [size, size, 3 + dim_num])
         #bias includes 3 kinds of peephole, 3+dim_num+2+dim_num
         self.create_bias_parameter(bias, size * (5 + 2 * dim_num))
 
@@ -2929,8 +2929,8 @@ class GruStepLayer(LayerBase):
                  active_gate_type="sigmoid",
                  bias=True,
                  **xargs):
-        super(GruStepLayer, self).__init__(name, 'gru_step', size, inputs, **
-                                           xargs)
+        super(GruStepLayer, self).__init__(name, 'gru_step', size, inputs,
+                                           **xargs)
         config_assert(len(self.inputs) == 2, 'GruStepLayer must have 2 input')
         input_layer0 = self.get_input_layer(0)
         input_layer1 = self.get_input_layer(1)

python/paddle/utils/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__all__ = ['dump_config']
\ No newline at end of file
+__all__ = ['dump_config']