提交 dad11db9 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #807 from reyoung/feature/fix_dead_link_in_rec

Fix dead link after rearrange english documentation.
.. _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
......
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
----------------
......
.. _api_trainer_config_helpers_data_sources:
DataSources
===========
......
......@@ -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
......
......@@ -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
......
......@@ -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)
```eval_rst
.. _cmd_line_index_en:
```
# How to Set Command-line Parameters
* [Use Case](use_case_en.md)
......
```eval_rst
.. _demo_ml_dataset_en:
```
# MovieLens Dataset
The [MovieLens Dataset](http://grouplens.org/datasets/movielens/) was collected by GroupLens Research.
......
......@@ -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_en` 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_en` 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'
......
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