Merge pull request #620 from gangliao/docstructure

English Document Structure

doc/about/index.rst

+ABOUT
+=======
+
+PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform,
+which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
+
+PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended.
+We hope to build an active open source community both by providing feedback and by actively contributing to the source code.
+
+
+Credits
+--------
+
+We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/blob/develop/authors>`_ of PaddlePaddle!

doc/algorithm/rnn/bi_lstm.jpg

-../../demo/sentiment_analysis/bi_lstm.jpg
\ No newline at end of file

doc/algorithm/rnn/encoder-decoder-attention-model.png

-../../demo/text_generation/encoder-decoder-attention-model.png
\ No newline at end of file

doc/ui/data_provider/index.rst → doc/api/data_provider/index.rst

-DataProvider Introduction
-=========================
+Introduction
+==============
 DataProvider is a module that loads training or testing data into cpu or gpu
 memory for the following triaining or testing process.
 

doc/ui/data_provider/pydataprovider2.rst → doc/api/data_provider/pydataprovider2.rst

-How to use PyDataProvider2
-==========================
+PyDataProvider2
+=================
 
 We highly recommand users to use PyDataProvider2 to provide training or testing
 data to PaddlePaddle. The user only needs to focus on how to read a single

doc/api/index.rst

+API
+====
+
+DataProvider API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  data_provider/index.rst
+  data_provider/pydataprovider2.rst
+
+Model Config API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  trainer_config_helpers/index.rst
+  trainer_config_helpers/optimizers.rst
+  trainer_config_helpers/data_sources.rst
+  trainer_config_helpers/layers.rst
+  trainer_config_helpers/activations.rst 
+  trainer_config_helpers/poolings.rst
+  trainer_config_helpers/networks.rst
+  trainer_config_helpers/evaluators.rst
+  trainer_config_helpers/attrs.rst
+
+
+Applications API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  predict/swig_py_paddle_en.rst
\ No newline at end of file

doc/ui/predict/swig_py_paddle_en.rst → doc/api/predict/swig_py_paddle_en.rst

-Python Prediction API
-=====================
+Python Prediction
+==================
 
 PaddlePaddle offers a set of clean prediction interfaces for python with the help of
 SWIG. The main steps of predict values in python are:

doc/ui/api/trainer_config_helpers/attrs.rst → doc/api/trainer_config_helpers/attrs.rst

-Parameter and Extra Layer Attribute
-===================================
+Parameter Attributes
+=======================
 
 ..  automodule:: paddle.trainer_config_helpers.attrs
     :members:

doc/cluster/index.rst

-Cluster Train
-====================
-
-.. toctree::
-  :glob:
-
-  opensource/cluster_train.md
-  internal/index.md

doc/dev/index.rst

-Development Guide
-=================
-
-..  toctree::
-  :maxdepth: 1
-
-  layer.md
-  new_layer/new_layer.rst
-  ../source/index.md

doc/dev/layer.md

-# Layer Documents
-
-* [Layer Source Code Document](../source/gserver/layers/index.rst)
-* [Layer Python API Document](../ui/api/trainer_config_helpers/index.rst)

doc/introduction/index.md → doc/getstarted/basic_usage/basic_usage.rst

-# Introduction
+Basic Usage
+=============
 
 PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on.
 
-## 1. A Classic Problem
+1. A Classic Problem
+---------------------
 
-Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - <a href="https://en.wikipedia.org/wiki/Simple_linear_regression">**simple linear regression**</a> : you have observed a set of two-dimensional data points of `X` and `Y`, where `X` is an explanatory variable and `Y` is corresponding dependent variable, and you want to recover the underlying correlation between `X` and `Y`. Linear regression can be used in many practical scenarios. For example, `X` can be a variable about house size, and `Y` a variable about house price. You can build a model that captures relationship between them by observing real estate markets.
+Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression <https://en.wikipedia.org/wiki/Simple_linear_regression>`_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets.
 
-## 2. Prepare the Data
+2. Prepare the Data
+--------------------
 
-Suppose the true relationship can be characterized as `Y = 2X + 0.3`, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types.
+Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types.
 
-```python
-# dataprovider.py
-from paddle.trainer.PyDataProvider2 import *
-import random
+    .. code-block:: python
 
-# define data types of input: 2 real numbers
-@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False)
-def process(settings, input_file):
-    for i in xrange(2000):
-        x = random.random()
-        yield [x], [2*x+0.3]
-```
+        # dataprovider.py
+        from paddle.trainer.PyDataProvider2 import *
+        import random
 
-## 3. Train a NeuralNetwork in PaddlePaddle
+        # define data types of input: 2 real numbers
+        @provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False)
+        def process(settings, input_file):
+            for i in xrange(2000):
+                x = random.random()
+                yield [x], [2*x+0.3]
 
-To recover this relationship between `X` and `Y`, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line `Y' = wX + b` , then we gradually adapt `w` and `b` to minimize the difference between `Y'` and `Y`. Here is what it looks like in PaddlePaddle:
+3. Train a NeuralNetwork
+-------------------------
 
-```python
-# trainer_config.py
-from paddle.trainer_config_helpers import *
+To recover this relationship between ``X`` and ``Y``, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line ``Y' = wX + b`` , then we gradually adapt ``w`` and ``b`` to minimize the difference between ``Y'`` and ``Y``. Here is what it looks like in PaddlePaddle:
 
-# 1. read data. Suppose you saved above python code as dataprovider.py
-data_file = 'empty.list'
-with open(data_file, 'w') as f: f.writelines(' ')
-define_py_data_sources2(train_list=data_file, test_list=None, 
-        module='dataprovider', obj='process',args={})
+    .. code-block:: python
 
-# 2. learning algorithm
-settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
+        # trainer_config.py
+        from paddle.trainer_config_helpers import *
 
-# 3. Network configuration
-x = data_layer(name='x', size=1)
-y = data_layer(name='y', size=1)
-y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
-cost = regression_cost(input=y_predict, label=y)
-outputs(cost)
-```
+        # 1. read data. Suppose you saved above python code as dataprovider.py
+        data_file = 'empty.list'
+        with open(data_file, 'w') as f: f.writelines(' ')
+        define_py_data_sources2(train_list=data_file, test_list=None, 
+                module='dataprovider', obj='process',args={})
+
+        # 2. learning algorithm
+        settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
+
+        # 3. Network configuration
+        x = data_layer(name='x', size=1)
+        y = data_layer(name='y', size=1)
+        y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
+        cost = regression_cost(input=y_predict, label=y)
+        outputs(cost)
 
 Some of the most fundamental usages of PaddlePaddle are demonstrated:
 
@@ -55,46 +59,51 @@ Some of the most fundamental usages of PaddlePaddle are demonstrated:
 -  The second part describes learning algorithm. It defines in what ways adjustments are made to model parameters. PaddlePaddle provides a rich set of optimizers, but a simple momentum based optimizer will suffice here, and it processes 12 data points each time.
 
 -  Finally, the network configuration. It usually is as simple as "stacking" layers. Three kinds of layers are used in this configuration:
-	-  **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for `X` and `Y`.
+	-  **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for ``X`` and ``Y``.
 	-  **FC Layer**: FC layer is short for Fully Connected Layer, which connects all the input units to current layer and does the actual computation specified as activation function. Computation layers like this are the fundamental building blocks of a deeper model.
 	-  **Cost Layer**: in training phase, cost layers are usually the last layers of the network. They measure the performance of current model, and provide guidence to adjust parameters.
 
 Now that everything is ready, you can train the network with a simple command line call:
- ```
- paddle train --config=trainer_config.py --save_dir=./output --num_passes=30
- ```
 
-This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path `./output`. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess.
+    .. code-block:: bash
+ 
+        paddle train --config=trainer_config.py --save_dir=./output --num_passes=30
+ 
+
+This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path ``./output``. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess.
+
 
+4. Evaluate the Model
+-----------------------
 
-## 4. Evaluate the Model
+Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: ``w=2, b=0.3``, thus a better option is to check out model parameters directly.
 
-Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: `w=2, b=0.3`, thus a better option is to check out model parameters directly.
+In PaddlePaddle, training is just to get a collection of model parameters, which are ``w`` and ``b`` in this case. Each parameter is saved in an individual file in the popular ``numpy`` array format. Here is the code that reads parameters from last pass.
 
-In PaddlePaddle, training is just to get a collection of model parameters, which are `w` and `b` in this case. Each parameter is saved in an individual file in the popular `numpy` array format. Here is the code that reads parameters from last pass.
+    .. code-block:: python
 
-```python
-import numpy as np
-import os
+        import numpy as np
+        import os
 
-def load(file_name):
-    with open(file_name, 'rb') as f:
-        f.read(16) # skip header for float type.
-        return np.fromfile(f, dtype=np.float32)
-        
-print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b'))
-# w=1.999743, b=0.300137
-```
+        def load(file_name):
+            with open(file_name, 'rb') as f:
+                f.read(16) # skip header for float type.
+                return np.fromfile(f, dtype=np.float32)
+                
+        print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b'))
+        # w=1.999743, b=0.300137
 
-<center> ![](./parameters.png) </center>
+    .. image:: parameters.png
+        :align: center
 
-Although starts from a random guess, you can see that value of `w` changes quickly towards 2 and `b` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer.
+Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer.
 
-There, you have recovered the underlying pattern between `X` and `Y` only from observed data.
+There, you have recovered the underlying pattern between ``X`` and ``Y`` only from observed data.
 
 
-## 5. Where to Go from Here
+5. Where to Go from Here
+-------------------------
 
-- <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>
+- `Install and Build <../build_and_install/index.html>`_
+- `Tutorials <../demo/quick_start/index_en.html>`_
+- `Example and Demo <../demo/index.html>`_

doc/build/index.rst → doc/getstarted/build_and_install/index.rst

@@ -8,8 +8,6 @@ Install PaddlePaddle
     :maxdepth: 1
     :glob:
 
-    install_*
-    internal/install_from_jumbo.md
     docker_install.rst
     ubuntu_install.rst
 
@@ -24,5 +22,4 @@ Build from Source
     :maxdepth: 1
     :glob:
 
-    build_from_source.md
-    contribute_to_paddle.md
+    build_from_source.md
\ No newline at end of file

doc/getstarted/index.rst

+GET STARTED
+============
+
+..  toctree::
+  :maxdepth: 2
+
+  build_and_install/index.rst
+  basic_usage/basic_usage.rst

doc/cluster/opensource/cluster_train.md → doc/howto/cluster/cluster_train.md

-# Distributed Training
+# How to Run Distributed Training
 
 In this article, we explain how to run distributed Paddle training jobs on clusters.  We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation).
 
@@ -9,7 +9,7 @@ In this article, we explain how to run distributed Paddle training jobs on clust
 1. Aforementioned scripts use a Python library [fabric](http://www.fabfile.org/) to run SSH commands.  We can use `pip` to install fabric:
 
    ```bash
-pip install fabric
+   pip install fabric
    ```
 
 1. We need to install PaddlePaddle on all nodes in the cluster.  To enable GPUs, we need to install CUDA in `/usr/local/cuda`; otherwise Paddle would report errors at runtime.

doc/howto/cmd_parameter/index.md

+# How to Set Command-line Parameters
+
+* [Use Case](use_case.md)
+* [Arguments](arguments.md)
+* [Detailed Descriptions](detail_introduction.md)

doc/build/contribute_to_paddle.md → doc/howto/contribute_to_paddle.md

-# Contribute Code
+# How to Contribute Code
 
 We sincerely appreciate your contributions. You can use fork and pull request
 workflow to merge your code. 

doc/algorithm/index.rst → doc/howto/deep_model/index.rst

-Algorithm Tutorial
-==================
+How to Configure Deep Models
+============================
 
 ..  toctree::
   :maxdepth: 1

doc/algorithm/rnn/rnn.rst → doc/howto/deep_model/rnn/rnn.rst

@@ -42,7 +42,7 @@ Simple Gated Recurrent Neural Network
 
 Recurrent neural network process a sequence at each time step sequentially. An example of the architecture of LSTM is listed below.
 
-.. image:: ./bi_lstm.jpg
+.. image:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg
 	 :align: center
 
 Generally speaking, a recurrent network perform the following operations from :math:`t=1` to :math:`t=T`, or reversely from :math:`t=T` to :math:`t=1`.
@@ -101,7 +101,7 @@ Sequence to Sequence Model with Attention
 -----------------------------------------
 We will use the sequence to sequence model with attention as an example to demonstrate how you can configure complex recurrent neural network models. An illustration of the sequence to sequence model with attention is shown in the following figure.
 
-.. image:: ./encoder-decoder-attention-model.png
+.. image:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png
  	 :align: center
 
 In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.

doc/howto/index.rst

+HOW TO
+=======
+
+Usage
+-------
+
+..  toctree::
+  :maxdepth: 1
+
+  cmd_parameter/index.md
+  deep_model/index.rst
+  cluster/cluster_train.md
+
+Development
+------------
+
+..  toctree::
+  :maxdepth: 1
+
+  new_layer/index.rst
+  contribute_to_paddle.md
+
+Optimization
+-------------
+
+..  toctree::
+  :maxdepth: 1
+
+  optimization/index.rst

doc/dev/new_layer/new_layer.rst → doc/howto/new_layer/index.rst

-==================
-Writing New Layers
-==================
+=======================
+How to Write New Layers
+=======================
 
 This tutorial will guide you to write customized layers in PaddlePaddle. We will utilize fully connected layer as an example to guide you through the following steps for writing a new layer.
 

doc/optimization/index.rst → doc/howto/optimization/index.rst

-Performance Tuning
-==================
+How to Tune GPU Performance
+===========================
 
 .. toctree::
   :maxdepth: 3

doc/index.rst

@@ -4,8 +4,9 @@ PaddlePaddle Documentation
 ..  toctree::
   :maxdepth: 1
 
-  introduction/index.md
-  user_guide.rst
-  dev/index.rst
-  algorithm/index.rst
-  optimization/index.rst
+  getstarted/index.rst
+  tutorials/index.md
+  howto/index.rst
+  api/index.rst
+  about/index.rst 
+ 
\ No newline at end of file

doc/introduction/parameters.png

-../../doc_cn/introduction/parameters.png
\ No newline at end of file

doc/demo/index.md → doc/tutorials/index.md

-# Examples and demos
+# TUTORIALS
 There are serveral examples and demos here.
 
 ## Image

doc/demo/semantic_role_labeling/semantic_role_labeling.md → doc/tutorials/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
-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(predicate_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(len(word_dict)),
-        integer_value_sequence(2),
-        integer_value_sequence(len(label_dict))]
-```
-The corresponding data iterator is as following:
-```
-@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
-          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_name):
-    with open(file_name, 'r') as fdata:
-        for line in fdata:
-            sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2,  mark, label = \
-                line.strip().split('\t')
-
-            words = sentence.split()
-            sen_len = len(words)
-            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
-
-            predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
-            ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
-            ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
-            ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
-            ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
-            ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
-
-            marks = mark.split()
-            mark_slot = [int(w) for w in marks]
-
-            label_list = label.split()
-            label_slot = [settings.label_dict.get(w) for w in label_list]
-            yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
-                  ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
-```
-The `process`function yield 9 lists which are 8 features and label.
- 
-### 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. 
-
-Nine `data_layer` load instances from data provider. Eight 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 \
-  --use_gpu=0 \
-  --log_period=5000 \
-  --trainer_count=1 \
-  --show_parameter_stats_period=5000 \
-  --save_dir=./output \
-  --num_passes=10000 \
-  --average_test_period=10000000 \
-  --init_model_path=./data \
-  --load_missing_parameter_strategy=rand \
-  --test_all_data_in_one_period=1 \
-2>&1 | tee 'train.log'
-```
-
--  \--config=./db_lstm.py : network config file.
--  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
--  \--log_period=500: print log every 20 batches.
--  \--trainer_count=1: set thread number (or GPU count).
--  \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
--  \--save_dir=./output: output path to save models.
--  \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
--  \--average_test_period=10000000:  do test on average parameter every average_test_period batches
--  \--init_model_path=./data: parameter initialization path 
--  \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
--  \--test_all_data_in_one_period=1: test all data in one period
-
-
-After training, the models  will be saved in directory `output`. Our training curve is as following:
-<center>
-![pic](./curve.jpg)
-</center>
-
-### 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
-  - \--test_all_data_in_one_period=1: test all data in 1 period
-  
-
-### 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 $best_model_path \
-     -l $label_file \
-     -p $predicate_dict_file  \
-     -d $dict_file \
-     -i $input_file \
-     -o $output_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
+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(predicate_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(len(word_dict)),
+        integer_value_sequence(2),
+        integer_value_sequence(len(label_dict))]
+```
+The corresponding data iterator is as following:
+```
+@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
+          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
+def process(settings, file_name):
+    with open(file_name, 'r') as fdata:
+        for line in fdata:
+            sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2,  mark, label = \
+                line.strip().split('\t')
+
+            words = sentence.split()
+            sen_len = len(words)
+            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
+
+            predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
+            ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
+            ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
+            ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
+            ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
+            ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
+
+            marks = mark.split()
+            mark_slot = [int(w) for w in marks]
+
+            label_list = label.split()
+            label_slot = [settings.label_dict.get(w) for w in label_list]
+            yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
+                  ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
+```
+The `process`function yield 9 lists which are 8 features and label.
+ 
+### 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. 
+
+Nine `data_layer` load instances from data provider. Eight 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 \
+  --use_gpu=0 \
+  --log_period=5000 \
+  --trainer_count=1 \
+  --show_parameter_stats_period=5000 \
+  --save_dir=./output \
+  --num_passes=10000 \
+  --average_test_period=10000000 \
+  --init_model_path=./data \
+  --load_missing_parameter_strategy=rand \
+  --test_all_data_in_one_period=1 \
+2>&1 | tee 'train.log'
+```
+
+-  \--config=./db_lstm.py : network config file.
+-  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
+-  \--log_period=500: print log every 20 batches.
+-  \--trainer_count=1: set thread number (or GPU count).
+-  \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
+-  \--save_dir=./output: output path to save models.
+-  \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
+-  \--average_test_period=10000000:  do test on average parameter every average_test_period batches
+-  \--init_model_path=./data: parameter initialization path 
+-  \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
+-  \--test_all_data_in_one_period=1: test all data in one period
+
+
+After training, the models  will be saved in directory `output`. Our training curve is as following:
+<center>
+![pic](./curve.jpg)
+</center>
+
+### 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
+  - \--test_all_data_in_one_period=1: test all data in 1 period
+  
+
+### 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 $best_model_path \
+     -l $label_file \
+     -p $predicate_dict_file  \
+     -d $dict_file \
+     -i $input_file \
+     -o $output_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/ui/api/trainer_config_helpers/index.rst

-Model Config Interface
-======================
-
-.. toctree::
-  :maxdepth: 1
-
-  optimizers.rst
-  data_sources.rst
-  layers.rst
-  activations.rst 
-  poolings.rst
-  networks.rst
-  evaluators.rst
-  attrs.rst

doc/ui/index.md

-# User Interface
-
-## Data Provider
-
-* [Introduction](data_provider/index.rst)
-* [PyDataProvider2](data_provider/pydataprovider2.rst)
-
-## API Reference
-
-* [Model Config Interface](api/trainer_config_helpers/index.md)
-
-## Command Line Argument
-
-* [Use Case](cmd_argument/use_case.md)
-* [Argument Outline](cmd_argument/argument_outline.md)
-* [Detailed Descriptions](cmd_argument/detail_introduction.md)
-
-## Predict
-
-* [Python Prediction API](predict/swig_py_paddle_en.rst)

doc/user_guide.rst

-User Guide
-==========
-
-..  toctree::
-  :maxdepth: 1
-
-  demo/quick_start/index_en.md
-  build/index.rst
-  build/contribute_to_paddle.md
-  ui/index.md
-  ui/api/trainer_config_helpers/index.rst
-  demo/index.md
-  cluster/index.md