Fix dead link after rearrange english documentation.

* Fix #803

doc/api/data_provider/pydataprovider2_en.rst

+..  _api_pydataprovider:
+
 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_en.rst

 API
-====
+===
 
 DataProvider API
 ----------------
@@ -10,6 +10,8 @@ DataProvider API
     data_provider/index_en.rst
     data_provider/pydataprovider2_en.rst
 
+..  _api_trainer_config:
+
 Model Config API
 ----------------
 

doc/api/trainer_config_helpers/data_sources.rst

+..  _api_trainer_config_helpers_data_sources:
+
 DataSources
 ===========
 

doc/api/trainer_config_helpers/layers.rst

@@ -20,6 +20,8 @@ LayerOutput
 Data layer
 ===========
 
+..  _api_trainer_config_helpers_layers_data_layer:
+
 data_layer
 ----------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -29,6 +31,8 @@ data_layer
 Fully Connected Layers
 ======================
 
+..  _api_trainer_config_helpers_layers_fc_layer:
+
 fc_layer
 --------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -68,6 +72,8 @@ img_conv_layer
     :members: img_conv_layer
     :noindex:
 
+..  _api_trainer_config_helpers_layers_context_projection:
+
 context_projection 
 ------------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -185,6 +191,8 @@ mixed_layer
     :members: mixed_layer
     :noindex:
 
+..  _api_trainer_config_helpers_layers_embedding_layer:
+
 embedding_layer
 ---------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -237,6 +245,8 @@ trans_full_matrix_projection
 Aggregate Layers
 ================
 
+..  _api_trainer_config_helpers_layers_pooling_layer:
+
 pooling_layer
 -------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -333,6 +343,8 @@ tensor_layer
     :members: tensor_layer
     :noindex:
 
+..  _api_trainer_config_helpers_layers_cos_sim:
+
 cos_sim
 -------
 ..  automodule:: paddle.trainer_config_helpers.layers

doc/api/trainer_config_helpers/networks.rst

@@ -13,6 +13,8 @@ sequence_conv_pool
     :members: sequence_conv_pool
     :noindex:
 
+..  _api_trainer_config_helpers_network_text_conv_pool:
+
 text_conv_pool
 --------------
 ..  automodule:: paddle.trainer_config_helpers.networks

doc/conf.py.in

@@ -144,5 +144,6 @@ def setup(app):
     # no c++ API for now
     app.add_config_value('recommonmark_config', {
             'url_resolver': lambda url: github_doc_root + url,
+	    'enable_eval_rst': True,
             }, True)
     app.add_transform(AutoStructify)

doc/howto/cmd_parameter/index_en.md

+```eval_rst
+..  _cmd_line_index:
+```
 # How to Set Command-line Parameters
 
 * [Use Case](use_case_en.md)

doc/tutorials/rec/ml_dataset_en.md

+```eval_rst
+..  _demo_ml_dataset:
+
+```
+
 # MovieLens Dataset
 
 The [MovieLens Dataset](http://grouplens.org/datasets/movielens/) was collected by GroupLens Research.

doc/tutorials/rec/ml_regression_en.rst

@@ -16,7 +16,7 @@ Data Preparation
 ````````````````
 Download and extract dataset
 ''''''''''''''''''''''''''''
-We use `movielens 1m dataset <ml_dataset.html>`_ here. 
+We use :ref:`demo_ml_dataset` here. 
 To download and unzip the dataset, simply run the following commands.
 
 ..  code-block:: bash
@@ -239,26 +239,16 @@ Then we combine each features of movie into one movie feature by a
 get one user feature. Then we calculate the cosine similarity of these two
 features.
 
-In these network, we use several api in `trainer_config_helpers
-<../../ui/api/trainer_config_helpers/index.html>`_. There are
-
-*  Data Layer, `data_layer 
-   <../../ui/api/trainer_config_helpers/layers.html#id1>`_
-*  Fully Connected Layer, `fc_layer
-   <../../ui/api/trainer_config_helpers/layers.html#fc-layer>`_
-*  Embedding Layer, `embedding_layer
-   <../../ui/api/trainer_config_helpers/layers.html#embedding-layer>`_
-*  Context Projection Layer, `context_projection
-   <../../ui/api/trainer_config_helpers/layers.html#context-projection>`_
-*  Pooling Layer, `pooling_layer
-   <../../ui/api/trainer_config_helpers/layers.html#pooling-layer>`_
-*  Cosine Similarity Layer, `cos_sim
-   <../../ui/api/trainer_config_helpers/layers.html#cos-sim>`_
-*  Text Convolution Pooling Layer, `text_conv_pool
-   <../../ui/api/trainer_config_helpers/networks.html
-   #trainer_config_helpers.networks.text_conv_pool>`_
-*  Declare Python Data Sources, `define_py_data_sources2
-   <../../ui/api/trainer_config_helpers/data_sources.html>`_
+In these network, we use several api in :ref:`api_trainer_config` . There are
+
+*  Data Layer, :ref:`api_trainer_config_helpers_layers_data_layer`
+*  Fully Connected Layer, :ref:`api_trainer_config_helpers_layers_fc_layer`
+*  Embedding Layer, :ref:`api_trainer_config_helpers_layers_embedding_layer`
+*  Context Projection Layer, :ref:`api_trainer_config_helpers_layers_context_projection`
+*  Pooling Layer, :ref:`api_trainer_config_helpers_layers_pooling_layer`
+*  Cosine Similarity Layer, :ref:`api_trainer_config_helpers_layers_cos_sim`
+*  Text Convolution Pooling Layer, :ref:`api_trainer_config_helpers_network_text_conv_pool`
+*  Declare Python Data Sources :ref:`api_trainer_config_helpers_data_sources`.
 
 Data Provider
 '''''''''''''
@@ -274,7 +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 `there <../../ui/data_provider/pydataprovider2.html>`_.
+The data provider details document see :ref:`api_pydataprovider`.
 
 Train
 `````
@@ -290,8 +280,7 @@ The run.sh is shown as follow:
 It just start a paddle training process, write the log to `log.txt`,
 then print it on screen.
 
-Each command line argument in :code:`run.sh`, please refer to the `command line
-arguments <../../ui/index.html#command-line-argument>`_ page. The short description of these arguments is shown as follow.
+Each command line argument in :code:`run.sh`, please refer to the :ref:`cmd_line_index` page. The short description of these arguments is shown as follow.
 
 *  config\: Tell paddle which file is neural network configuration.
 *  save_dir\: Tell paddle save model into './output'