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refine doc structure and revise some words

上级 7a846e4c
About ABOUT
======= =======
Credits
--------
PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, 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. 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. 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. 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! We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/blob/develop/authors>`_ of PaddlePaddle!
DataProvider Introduction Introduction
========================= ==============
DataProvider is a module that loads training or testing data into cpu or gpu DataProvider is a module that loads training or testing data into cpu or gpu
memory for the following triaining or testing process. memory for the following triaining or testing process.
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How to use PyDataProvider2 PyDataProvider2
========================== =================
We highly recommand users to use PyDataProvider2 to provide training or testing 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 data to PaddlePaddle. The user only needs to focus on how to read a single
......
# API
## Data Provider
* [Introduction](data_provider/index.rst)
* [PyDataProvider2](data_provider/pydataprovider2.rst)
## Trainer Configuration
* [Model Config Interface](trainer_config_helpers/index.rst)
## Predict
* [Python Prediction API](predict/swig_py_paddle_en.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
Python Prediction API Python Prediction
===================== ==================
PaddlePaddle offers a set of clean prediction interfaces for python with the help of PaddlePaddle offers a set of clean prediction interfaces for python with the help of
SWIG. The main steps of predict values in python are: SWIG. The main steps of predict values in python are:
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Parameter and Extra Layer Attribute Parameter Attributes
=================================== =======================
.. automodule:: paddle.trainer_config_helpers.attrs .. automodule:: paddle.trainer_config_helpers.attrs
:members: :members:
Model Config Interface
======================
.. toctree::
:maxdepth: 1
optimizers.rst
data_sources.rst
layers.rst
activations.rst
poolings.rst
networks.rst
evaluators.rst
attrs.rst
Introduction GET STARTED
============ ============
.. toctree:: .. toctree::
......
../../../tutorials/sentiment_analysis/bi_lstm.jpg
\ No newline at end of file
../../../tutorials/text_generation/encoder-decoder-attention-model.png
\ No newline at end of file
# 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). 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).
......
# Command Line Argument # How to Set Command-line Parameters
* [Use Case](use_case.md) * [Use Case](use_case.md)
* [Argument Outline](argument_outline.md) * [Arguments](arguments.md)
* [Detailed Descriptions](detail_introduction.md) * [Detailed Descriptions](detail_introduction.md)
# Contribute Code # How to Contribute Code
We sincerely appreciate your contributions. You can use fork and pull request We sincerely appreciate your contributions. You can use fork and pull request
workflow to merge your code. workflow to merge your code.
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Algorithm Configuration How to Configure Deep Models
======================= ============================
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
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...@@ -42,7 +42,7 @@ Simple Gated Recurrent Neural Network ...@@ -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. 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 :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`. 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 ...@@ -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. 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 :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`. 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`.
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Development Guide
=================
.. toctree::
:maxdepth: 2
layer.md
new_layer/new_layer.rst
source/index.rst
# Layer Documents
* [Layer Python API](../../api/trainer_config_helpers/index.rst)
* [Layer Source Code](source/gserver/layers.rst)
* [Writing New Layers](new_layer/new_layer.rst)
How to HOW TO
======= =======
Usage
-------
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
cmd_argument/index.md cmd_parameter/index.md
deep_model/index.rst
cluster/cluster_train.md cluster/cluster_train.md
algorithm/index.rst
Development
------------
.. toctree::
:maxdepth: 1
new_layer/index.rst
contribute_to_paddle.md
Optimization
-------------
.. toctree::
:maxdepth: 1
optimization/index.rst optimization/index.rst
dev/index.rst
contribute_to_paddle.md
\ No newline at end of file
================== =======================
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. 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.
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Performance Tuning How to Tune GPU Performance
================== ===========================
.. toctree:: .. toctree::
:maxdepth: 3 :maxdepth: 3
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...@@ -4,7 +4,7 @@ PaddlePaddle Documentation ...@@ -4,7 +4,7 @@ PaddlePaddle Documentation
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
introduction/index.rst getstarted/index.rst
tutorials/index.md tutorials/index.md
howto/index.rst howto/index.rst
api/index.rst api/index.rst
......
# Tutorials # TUTORIALS
There are serveral examples and demos here. There are serveral examples and demos here.
## Image ## Image
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