Merge pull request #806 from luotao1/link

fix some dead links in doc/

Author: qingqing01

doc/api/data_provider/pydataprovider2_en.rst

@@ -1,4 +1,4 @@
-..  _api_pydataprovider:
+..  _api_pydataprovider2_en:
 
 PyDataProvider2
 ===============
@@ -104,6 +104,8 @@ And PaddlePadle will do all of the rest things\:
 
 Is this cool?
 
+..  _api_pydataprovider2_en_sequential_model:
+
 DataProvider for the sequential model
 -------------------------------------
 A sequence model takes sequences as its input. A sequence is made up of several

doc/api/predict/swig_py_paddle_en.rst

@@ -23,7 +23,7 @@ python's :code:`help()` function. Let's walk through the above python script:
 
 * At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
   PaddlePaddle with command line arguments, for more about command line arguments
-  see `Command Line Arguments <../cmd_argument/detail_introduction.html>`_.
+  see :ref:`cmd_detail_introduction_en` .
 * Parse the configuration file that is used in training with :code:`parse_config()`.
   Because data to predict with always have no label, and output of prediction work
   normally is the output layer rather than the cost layer, so you should modify
@@ -36,7 +36,7 @@ python's :code:`help()` function. Let's walk through the above python script:
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
       class DataProviderConverter that can accept the same input data with
       PyDataProvider2, for more information please refer to document
-      of `PyDataProvider2 <../data_provider/pydataprovider2.html>`_.
+      of :ref:`api_pydataprovider2_en` .
 * Do the prediction with :code:`forwardTest()`, which takes the converted
   input data and outputs the activations of the output layer.
 

doc/api/trainer_config_helpers/layers.rst

@@ -1,3 +1,5 @@
+..  _api_trainer_config_helpers_layers:
+
 ======
 Layers
 ======

doc/getstarted/basic_usage/index_en.rst

@@ -99,11 +99,3 @@ In PaddlePaddle, training is just to get a collection of model parameters, which
 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.
-
-
-5. Where to Go from Here
--------------------------
-
-- `Install and Build <../build_and_install/index.html>`_
-- `Tutorials <../demo/quick_start/index_en.html>`_
-- `Example and Demo <../demo/index.html>`_

doc/howto/cmd_parameter/detail_introduction_en.md

@@ -1,3 +1,7 @@
+```eval_rst
+..  _cmd_detail_introduction_en:
+```
+
 # Detail Description
 
 ## Common

doc/howto/deep_model/rnn/rnn_en.rst

@@ -30,7 +30,7 @@ Then at the :code:`process` function, each :code:`yield` function will return th
     yield src_ids, trg_ids, trg_ids_next
 
 
-For more details description of how to write a data provider, please refer to `PyDataProvider2 <../../ui/data_provider/index.html>`_. The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
+For more details description of how to write a data provider, please refer to :ref:`api_pydataprovider2_en` . The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
 
 ===============================================
 Configure Recurrent Neural Network Architecture
@@ -106,7 +106,7 @@ We will use the sequence to sequence model with attention as an example to demon
 
 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`.
 
-The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to `Layers <../../ui/api/trainer_config_helpers/layers_index.html>`_  for more details.
+The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to :ref:`api_trainer_config_helpers_layers` for more details.
 
 We also project the encoder vector to :code:`decoder_size` dimensional space, get the first instance of the backward recurrent network, and project it to :code:`decoder_size` dimensional space:
 
@@ -246,6 +246,6 @@ The code is listed below:
     outputs(beam_gen)
 
 
-Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to `Semantic Role Labeling Demo <../../demo/semantic_role_labeling/index.html>`_ for more details.
+Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling_en` for more details.
 
 The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`.

doc/howto/optimization/gpu_profiling_en.rst

@@ -51,7 +51,7 @@ In this tutorial, we will focus on nvprof and nvvp.
 :code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate
 above profilers. 
 
-.. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+.. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
    :language: c++
    :lines: 111-124
    :linenos:
@@ -77,7 +77,7 @@ As a simple example, consider the following:
 
 1. Add :code:`REGISTER_TIMER_INFO` and :code:`printAllStatus` functions (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 8-10,13
@@ -124,7 +124,7 @@ To use this command line profiler **nvprof**, you can simply issue the following
 
 1. Add :code:`REGISTER_GPU_PROFILER` function (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 6-7

doc/tutorials/embedding_model/index_en.md

@@ -93,7 +93,7 @@ where `train.sh` is almost the same as `demo/seqToseq/translation/train.sh`, the
 - `--init_model_path`: path of the initialization model, here is `data/paraphrase_model`
 - `--load_missing_parameter_strategy`: operations when model file is missing, here use a normal distibution to initialize the other parameters except for the embedding layer
 
-For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](../text_generation/text_generation.md).
+For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](../text_generation/index_en.md).
 
 ## Optional Function ##
 ###  Embedding Parameters Observation

doc/tutorials/rec/ml_regression_en.rst

@@ -264,7 +264,7 @@ In this :code:`dataprovider.py`, we should set\:
 * use_seq\: Whether this :code:`dataprovider.py` in sequence mode or not.
 * process\: Return each sample of data to :code:`paddle`.
 
-The data provider details document see :ref:`api_pydataprovider`.
+The data provider details document see :ref:`api_pydataprovider2_en`.
 
 Train
 `````

doc/tutorials/semantic_role_labeling/index_en.md

@@ -1,3 +1,7 @@
+```eval_rst
+..  _semantic_role_labeling_en:
+```
+
 # 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]: