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Introduction
==============
DataProvider is a module that loads training or testing data into cpu or gpu
memory for the following triaining or testing process.
For simple use, users can use Python :code:`PyDataProvider` to dynamically reads
the original data in any format or in any form, and then transfer them into a
data format PaddlePaddle requires. The process is extremly flexible and highly
customized, with sacrificing the efficiency only a little. This is extremly
useful when you have to dynamically generate certain kinds of data according to,
for example, the training performance.
Besides, users also can customize a C++ :code:`DataProvider` for a more
complex usage, or for a higher efficiency.
The following parameters are required to define in the PaddlePaddle network
configuration file (trainer_config.py): which DataProvider is chosen to used,
and specific parameters for DataProvider, including training file list
(train.list) and testing file list (test.list).
Train.list and test.list are simply two plain text files, which defines path
of training or testing data. It is recommended that directly placing them into
the training directory, and reference to them by using a relative path (
relative to the PaddePaddle program).
Testing or evaluating will not be performed during training if the test.list is
not set or set to None. Otherwise, PaddlePaddle will evaluate the trained model
by the specified tesing data while training, every testing period (a user
defined command line parameter in PaddlePaddle) to prevent over-fitting.
Each line of train.list and test.list is an absolute or relative path (relative
to the PaddePaddle program runtime) of data file. Fascinatingly more, each line
can also be a HDFS file path or a SQL connection string. As long as the user
assures how to access each file in DataProvider.
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.. _api_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
sample from the original data file by using PyDataProvider2, leaving all of the
trivial work, including, transfering data into cpu/gpu memory, shuffle, binary
serialization to PyDataProvider2. PyDataProvider2 uses multithreading and a
fanscinating but simple cache strategy to optimize the efficiency of the data
providing process.
DataProvider for the non-sequential model
-----------------------------------------
Here we use the MNIST handwriting recognition data as an example to illustrate
how to write a simple PyDataProvider.
MNIST is a handwriting classification data set. It contains 70,000 digital
grayscale images. Labels of the training sample range from 0 to 9. All the
images have been size-normalized and centered into images with the same size
of 28 x 28 pixels.
A small part of the original data as an example is shown as below:
.. literalinclude:: src/mnist_train.txt
Each line of the data contains two parts, separated by :code:`;`. The first part is
label of an image. The second part contains 28x28 pixel float values.
Just write path of the above data into train.list. It looks like this:
.. literalinclude:: src/train.list
The corresponding dataprovider is shown as below:
.. literalinclude:: src/mnist_provider.dict.py
The first line imports PyDataProvider2 package.
The main function is the process function, that has two parameters.
The first parameter is the settings, which is not used in this example.
The second parameter is the filename, that is exactly each line of train.list.
This parameter is passed to the process function by PaddlePaddle.
:code:`@provider` is a Python
`Decorator <http://www.learnpython.org/en/Decorators>`_ .
It sets some properties to DataProvider, and constructs a real PaddlePaddle
DataProvider from a very simple user implemented python function. It does not
matter if you are not familiar with `Decorator`_. You can keep it simple by
just taking :code:`@provider` as a fixed mark above the provider function you
implemented.
`input_types`_ defines the data format that a DataProvider returns.
In this example, it is set to a 28x28-dimensional dense vector and an integer
scalar, whose value ranges from 0 to 9.
`input_types`_ can be set to several kinds of input formats, please refer to the
document of `input_types`_ for more details.
The process method is the core part to construct a real DataProvider in
PaddlePaddle. It implements how to open the text file, how to read one sample
from the original text file, convert them into `input_types`_, and give them
back to PaddlePaddle process at line 23.
Note that data yielded by the process function must follow the same order that
`input_types`_ are defined.
With the help of PyDataProvider2, user can focus on how to generate ONE traning
sample by using keywords :code:`yield`.
:code:`yield` is a python keyword, and a concept related to it includes
:code:`generator`.
Only a few lines of codes need to be added into the training configuration file,
you can take this as an example.
.. literalinclude:: src/mnist_config.py
Here we specify training data by :code:`train.list`, and no testing data is specified.
The method which actually provide data is :code:`process`.
User also can use another style to provide data, which defines the
:code:`data_layer`'s name explicitly when `yield`. For example,
the :code:`dataprovider` is shown as below.
.. literalinclude:: src/mnist_provider.dict.py
:linenos:
If user did't give the :code:`data_layer`'s name, PaddlePaddle will use
the order of :code:`data_layer` definition roughly to determine which feature to
which :code:`data_layer`. This order may be not correct, so TO DEFINE THE
:code:`data_layer`'s NAMES EXPLICITLY IS THE RECOMMANDED WAY TO PROVIDER DATA.
Now, this simple example of using PyDataProvider is finished.
The only thing that the user should know is how to generte **one sample** from
**one data file**.
And PaddlePadle will do all of the rest things\:
* Form a training batch
* Shuffle the training data
* Read data with multithreading
* Cache the training data (Optional)
* CPU-> GPU double buffering.
Is this cool?
.. _api_pydataprovider2_sequential_model:
DataProvider for the sequential model
-------------------------------------
A sequence model takes sequences as its input. A sequence is made up of several
timesteps. The so-called timestep, is not necessary to have something to do
with time. It can also be explained to that the order of data are taken into
consideration into model design and training.
For example, the sentence can be interpreted as a kind of sequence data in NLP
tasks.
Here is an example on data proivider for English sentiment classification data.
The original input data are simple English text, labeled into positive or
negative sentiment (marked by 0 and 1 respectively).
A small part of the original data as an example can be found in the path below:
.. literalinclude:: src/sentimental_train.txt
The corresponding data provider can be found in the path below:
.. literalinclude:: src/sentimental_provider.py
This data provider for sequential model is a little more complex than that
for MINST dataset.
A new initialization method is introduced here.
The method :code:`on_init` is configured to DataProvider by :code:`@provider`'s
:code:`init_hook` parameter, and it will be invoked once DataProvider is
initialized. The :code:`on_init` function has the following parameters:
* The first parameter is the settings object.
* The rest parameters are passed by key word arguments. Some of them are passed
by PaddlePaddle, see reference for `init_hook`_.
The :code:`dictionary` object is a python dict object passed from the trainer
configuration file, and it maps word string to word id.
To pass these parameters into DataProvider, the following lines should be added
into trainer configuration file.
.. literalinclude:: src/sentimental_config.py
The definition is basically same as MNIST example, except:
* Load dictionary in this configuration
* Pass it as a parameter to the DataProvider
The `input_types` is configured in method :code:`on_init`. It has the same
effect to configure them by :code:`@provider`'s :code:`input_types` parameter.
However, the :code:`input_types` is set at runtime, so we can set it to
different types according to the input data. Input of the neural network is a
sequence of word id, so set :code:`seq_type` to :code:`integer_value_sequence`.
Durning :code:`on_init`, we save :code:`dictionary` variable to
:code:`settings`, and it will be used in :code:`process`. Note the settings
parameter for the process function and for the on_init's function are a same
object.
The basic processing logic is the same as MNIST's :code:`process` method. Each
sample in the data file is given back to PaddlePaddle process.
Thus, the basic usage of PyDataProvider is here.
Please refer to the following section reference for details.
Reference
---------
@provider
+++++++++
.. autofunction:: paddle.trainer.PyDataProvider2.provider
input_types
+++++++++++
PaddlePaddle has four data types, and three sequence types.
The four data types are:
* :code:`dense_vector`: dense float vector.
* :code:`sparse_binary_vector`: sparse binary vector, most of the value is 0, and
the non zero elements are fixed to 1.
* :code:`sparse_float_vector`: sparse float vector, most of the value is 0, and some
non zero elements can be any float value. They are given by the user.
* :code:`integer`: an integer scalar, that is especially used for label or word index.
The three sequence types are:
* :code:`SequenceType.NO_SEQUENCE` means the sample is not a sequence.
* :code:`SequenceType.SEQUENCE` means the sample is a sequence.
* :code:`SequenceType.SUB_SEQUENCE` means it is a nested sequence, that each timestep of
the input sequence is also a sequence.
Different input type has a defferenct input format. Their formats are shown
in the above table.
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| | NO_SEQUENCE | SEQUENCE | SUB_SEQUENCE |
+======================+=====================+===================================+================================================+
| dense_vector | [f, f, ...] | [[f, ...], [f, ...], ...] | [[[f, ...], ...], [[f, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
where f represents a float value, i represents an integer value.
init_hook
+++++++++
init_hook is a function that is invoked once the data provoder is initialized.
Its parameters lists as follows:
* The first parameter is a settings object, which is the same to :code:`settings`
in :code:`process` method. The object contains several attributes, including:
* :code:`settings.input_types`: the input types. Reference `input_types`_.
* :code:`settings.logger`: a logging object.
* The rest parameters are the key word arguments. It is made up of PaddpePaddle
pre-defined parameters and user defined parameters.
* PaddlePaddle-defined parameters including:
* :code:`is_train` is a bool parameter that indicates the DataProvider is used in
training or testing.
* :code:`file_list` is the list of all files.
* User-defined parameters args can be set in training configuration.
Note, PaddlePaddle reserves the right to add pre-defined parameter, so please
use :code:`**kwargs` in init_hook to ensure compatibility by accepting the
parameters which your init_hook does not use.
cache
+++++
DataProvider provides two simple cache strategy. They are:
* :code:`CacheType.NO_CACHE` means do not cache any data, then data is read at runtime by
the user implemented python module every pass.
* :code:`CacheType.CACHE_PASS_IN_MEM` means the first pass reads data by the user
implemented python module, and the rest passes will directly read data from
memory.
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API
===
DataProvider API
----------------
.. toctree::
:maxdepth: 1
data_provider/dataprovider_en.rst
data_provider/pydataprovider2_en.rst
.. _api_trainer_config:
Model Config API
----------------
.. toctree::
:maxdepth: 1
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
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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:
* Parse training configurations
* Construct GradientMachine
* Prepare data
* Predict
Here is a sample python script that shows the typical prediction process for the
MNIST classification problem. A complete sample code could be found at
:code:`src_root/doc/ui/predict/predict_sample.py`.
.. literalinclude:: src/predict_sample.py
:language: python
:lines: 15-18,90-100,101-104
The module that does the most of the job is py_paddle.swig_paddle, it's
generated by SWIG and has complete documents, for more details you can use
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 :ref:`cmd_detail_introduction` .
* 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
the configuration file accordingly before using it in the prediction work.
* Create a neural network with
:code:`swig_paddle.GradientMachine.createFromConfigproto()`, which takes the
parsed configuration :code:`conf.model_config` as argument. Then load the
trained parameters from the model with :code:`network.loadParameters()`.
* Create a data converter object of utility class :code:`DataProviderConverter`.
- 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 :ref:`api_pydataprovider2` .
* Do the prediction with :code:`forwardTest()`, which takes the converted
input data and outputs the activations of the output layer.
Here is a typical output:
.. code-block:: text
[{'id': None, 'value': array([[ 5.53018653e-09, 1.12194102e-05, 1.96644767e-09,
1.43630644e-02, 1.51111044e-13, 9.85625684e-01,
2.08823112e-10, 2.32777140e-08, 2.00186201e-09,
1.15501715e-08],
[ 9.99982715e-01, 1.27787406e-10, 1.72296313e-05,
1.49316648e-09, 1.36540484e-11, 6.93137714e-10,
2.70634608e-08, 3.48565123e-08, 5.25639710e-09,
4.48684503e-08]], dtype=float32)}]
:code:`value` is the output of the output layer, each row represents result of
the corresponding row in the input data, each element represents activation of
the corresponding neuron in the output layer.
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<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline"></a></h1>
<p>DataProvider is a module that loads training or testing data into cpu or gpu
memory for the following triaining or testing process.</p>
<p>For simple use, users can use Python <code class="code docutils literal"><span class="pre">PyDataProvider</span></code> to dynamically reads
the original data in any format or in any form, and then transfer them into a
data format PaddlePaddle requires. The process is extremly flexible and highly
customized, with sacrificing the efficiency only a little. This is extremly
useful when you have to dynamically generate certain kinds of data according to,
for example, the training performance.</p>
<p>Besides, users also can customize a C++ <code class="code docutils literal"><span class="pre">DataProvider</span></code> for a more
complex usage, or for a higher efficiency.</p>
<p>The following parameters are required to define in the PaddlePaddle network
configuration file (trainer_config.py): which DataProvider is chosen to used,
and specific parameters for DataProvider, including training file list
(train.list) and testing file list (test.list).</p>
<p>Train.list and test.list are simply two plain text files, which defines path
of training or testing data. It is recommended that directly placing them into
the training directory, and reference to them by using a relative path (
relative to the PaddePaddle program).</p>
<p>Testing or evaluating will not be performed during training if the test.list is
not set or set to None. Otherwise, PaddlePaddle will evaluate the trained model
by the specified tesing data while training, every testing period (a user
defined command line parameter in PaddlePaddle) to prevent over-fitting.</p>
<p>Each line of train.list and test.list is an absolute or relative path (relative
to the PaddePaddle program runtime) of data file. Fascinatingly more, each line
can also be a HDFS file path or a SQL connection string. As long as the user
assures how to access each file in DataProvider.</p>
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<h1>API<a class="headerlink" href="#api" title="Permalink to this headline"></a></h1>
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</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="api/v2/data/image.html#module-paddle.v2.image">paddle.v2.image (module)</a> <li><a href="api/v2/data/image.html#module-paddle.v2.image">paddle.v2.image (module)</a>
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<li><a href="api/v1/data_provider/pydataprovider2_en.html#paddle.trainer.PyDataProvider2.provider">provider() (in module paddle.trainer.PyDataProvider2)</a>
</li> </li>
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</tr></table> </tr></table>
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.. _api_dataprovider:
DataProvider的介绍
==================
DataProvider是PaddlePaddle负责提供数据的模块。其作用是将数据传入内存或显存，让神经网络可以进行训练或预测。用户可以通过简单使用Python接口 :ref:`api_pydataprovider2` ，来自定义传数据的过程。如果有更复杂的使用，或者需要更高的效率，用户也可以在C++端自定义一个 ``DataProvider`` 。
PaddlePaddle需要用户在网络配置（trainer_config.py）中定义使用哪种DataProvider，并且在DataProvider中实现如何访问训练文件列表（train.list）或测试文件列表（test.list）。
- train.list和test.list存放在本地（推荐直接存放到训练目录，以相对路径引用)。一般情况下，两者均为纯文本文件，其中每一行对应一个数据文件地址：
- 如果数据文件存于本地磁盘，这个地址则为它的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)。
- 地址也可以为hdfs文件路径，或者数据库连接路径等。
- 由于这个地址会被DataProvider使用，因此，如何解析该地址也是用户自定义DataProvider时需要考虑的地方。
- 如果没有设置test.list，或设置为None，那么在训练过程中不会执行测试操作；否则，会根据命令行参数指定的测试方式，在训练过程中进行测试，从而防止过拟合。
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.. _api_pydataprovider2:
PyDataProvider2的使用
=====================
PyDataProvider2是PaddlePaddle使用Python提供数据的推荐接口。该接口使用多线程读取数据，并提供了简单的Cache功能；同时可以使用户只关注如何从文件中读取每一条数据，而不用关心数据如何传输，如何存储等等。
.. contents::
MNIST的使用场景
---------------
我们以MNIST手写识别为例，来说明PyDataProvider2的简单使用场景。
样例数据
++++++++
MNIST是一个包含有70,000张灰度图片的数字分类数据集。样例数据 ``mnist_train.txt`` 如下：
.. literalinclude:: src/mnist_train.txt
其中每行数据代表一张图片，行内使用 ``;`` 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 ``train.list`` 即为这个数据文件的名字：
.. literalinclude:: src/train.list
dataprovider的使用
++++++++++++++++++
.. literalinclude:: src/mnist_provider.dict.py
- 首先，引入PaddlePaddle的PyDataProvider2包。
- 其次，定义一个Python的 `Decorator <http://www.learnpython.org/en/Decorators>`_ `@provider`_ 。用于将下一行的数据输入函数标记成一个PyDataProvider2，同时设置它的input_types属性。
- `input_types`_：设置这个PyDataProvider2返回什么样的数据。本例根据网络配置中 ``data_layer`` 的名字，显式指定返回的是一个28*28维的稠密浮点数向量和一个[0-9]的10维整数标签。
.. literalinclude:: src/mnist_config.py
:lines: 9-10
- 注意：如果用户不显示指定返回数据的对应关系，那么PaddlePaddle会根据layer的声明顺序，来确定对应关系。但这个关系可能不正确，所以推荐使用显式指定的方式来设置input_types。
- 最后，实现数据输入函数（如本例的 ``process`` 函数）。
- 该函数的功能是：打开文本文件，读取每一行，将行中的数据转换成与input_types一致的格式，然后返回给PaddlePaddle进程。注意，
- 返回的顺序需要和input_types中定义的顺序一致。
- 返回时，必须使用Python关键词 ``yield`` ，相关概念是 ``generator`` 。
- 一次yield调用，返回一条完整的样本。如果想为一个数据文件返回多条样本，只需要在函数中调用多次yield即可（本例中使用for循环进行多次调用）。
- 该函数具有两个参数：
- settings：在本例中没有使用，具体可以参考 `init_hook`_ 中的说明。
- filename：为 ``train.list`` 或 ``test.list`` 中的一行，即若干数据文件路径的某一个。
网络配置中的调用
++++++++++++++++
在网络配置里，只需要一行代码就可以调用这个PyDataProvider2，如，
.. literalinclude:: src/mnist_config.py
:lines: 1-7
训练数据是 ``train.list`` ，没有测试数据，调用的PyDataProvider2是 ``mnist_provider`` 模块中的 ``process`` 函数。
小结
+++++
至此，简单的PyDataProvider2样例就说明完毕了。对用户来说，仅需要知道如何从 **一个文件** 中读取 **一条样本** ，就可以将数据传送给PaddlePaddle了。而PaddlePaddle则会帮用户做以下工作：
* 将数据组合成Batch进行训练
* 对训练数据进行Shuffle
* 多线程的数据读取
* 缓存训练数据到内存(可选)
* CPU->GPU双缓存
是不是很简单呢？
时序模型的使用场景
------------------
样例数据
++++++++
时序模型是指数据的某一维度是一个序列形式，即包含时间步信息。所谓时间步信息，不一定和时间有关系，只是说明数据的顺序是重要的。例如，文本信息就是一个序列数据。
本例采用英文情感分类的数据，即将一段英文文本数据，分类成正面情绪和负面情绪两类(用0和1表示)。样例数据 ``sentimental_train.txt`` 如下：
.. literalinclude:: src/sentimental_train.txt
dataprovider的使用
++++++++++++++++++
相对MNIST而言，这个dataprovider较复杂，主要原因是增加了初始化机制 `init_hook`_。本例的 ``on_init`` 函数就是根据该机制配置的，它会在dataprovider创建的时候执行。
- 其中 ``input_types`` 和在 `@provider`_ 中配置的效果一致。本例中的输入特征是词ID的序列，因此使用 ``integer_value_sequence`` 类型来设置。
- 将 ``dictionary`` 存入settings对象，在 ``process`` 函数中使用。 dictionary是从网络配置中传入的dict对象，即一个将单词字符串映射到单词ID的字典。
.. literalinclude:: src/sentimental_provider.py
网络配置中的调用
++++++++++++++++
调用这个PyDataProvider2的方法，基本上和MNIST样例一致，除了
* 在配置中需要读取外部字典。
* 在声明DataProvider的时候传入dictionary作为参数。
.. literalinclude:: src/sentimental_config.py
:emphasize-lines: 12-14
参考(Reference)
---------------
@provider
+++++++++
``@provider`` 是一个Python的 `Decorator`_ ，可以将某一个函数标记成一个PyDataProvider2。如果不了解 `Decorator`_ 是什么也没关系，只需知道这是一个标记属性的方法就可以了。它包含的属性参数如下:
* input_types：数据输入格式。具体的格式说明，请参考 `input_types`_ 。
* should_shuffle：是不是要对数据做Shuffle。训练时默认shuffle，测试时默认不shuffle。
* min_pool_size：设置内存中最小暂存的数据条数，也是PaddlePaddle所能够保证的shuffle粒度。如果为-1，则会预先读取全部数据到内存中。
* pool_size： 设置内存中暂存的数据条数。如果为-1（默认），则不在乎内存暂存多少条数据。如果设置，则推荐大于训练时batch size的值，并且在内存足够的情况下越大越好。
* can_over_batch_size：是否允许暂存略微多余pool_size的数据。由于这样做可以避免很多死锁问题，一般推荐设置成True。
* calc_batch_size：可以传入一个函数，用于自定义每条数据的batch size（默认为1）。
* cache： 数据缓存的策略，具体请参考 `cache`_ 。
* init_hook：初始化时调用的函数，具体请参考 `init_hook`_ 。
* check：如果为true，会根据input_types检查数据的合法性。
* check_fail_continue：如果为true，那么当check出数据不合法时，会扔到这条数据，继续训练或预测。（对check=false的情况，没有作用）
input_types
+++++++++++
PaddlePaddle的数据包括四种主要类型，和三种序列模式。
四种数据类型：
* dense_vector：稠密的浮点数向量。
* sparse_binary_vector：稀疏的01向量，即大部分值为0，但有值的地方必须为1。
* sparse_float_vector：稀疏的向量，即大部分值为0，但有值的部分可以是任何浮点数。
* integer：整数标签。
三种序列模式：
* SequenceType.NO_SEQUENCE：不是一条序列
* SequenceType.SEQUENCE：是一条时间序列
* SequenceType.SUB_SEQUENCE： 是一条时间序列，且序列的每一个元素还是一个时间序列。
不同的数据类型和序列模式返回的格式不同，列表如下：
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| | NO_SEQUENCE | SEQUENCE | SUB_SEQUENCE |
+======================+=====================+===================================+================================================+
| dense_vector | [f, f, ...] | [[f, ...], [f, ...], ...] | [[[f, ...], ...], [[f, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+
其中，f代表一个浮点数，i代表一个整数。
注意：对sparse_binary_vector和sparse_float_vector，PaddlePaddle存的是有值位置的索引。例如，
- 对一个5维非序列的稀疏01向量 ``[0, 1, 1, 0, 0]`` ，类型是sparse_binary_vector，返回的是 ``[1, 2]`` 。
- 对一个5维非序列的稀疏浮点向量 ``[0, 0.5, 0.7, 0, 0]`` ，类型是sparse_float_vector，返回的是 ``[(1, 0.5), (2, 0.7)]`` 。
init_hook
+++++++++
init_hook可以传入一个函数。该函数在初始化的时候会被调用，其参数如下:
* 第一个参数是settings对象，它和数据传入函数的第一个参数（如本例中 ``process`` 函数的 ``settings`` 参数）必须一致。该对象具有以下两个属性：
* settings.input_types：数据输入格式，具体请参考 `input_types`_ 。
* settings.logger：一个logging对象。
* 其他参数使用 ``kwargs`` （key word arguments）传入，包括以下两种：
* PaddlePaddle定义的参数: 1）is_train：bool型参数，表示用于训练或预测；2）file_list：所有文件列表。
* 用户定义的参数：使用args在网络配置中设置。
注意：PaddlePaddle保留添加参数的权力，因此init_hook尽量使用 ``**kwargs`` 来接受不使用的函数以保证兼容性。
cache
+++++
PyDataProvider2提供了两种简单的Cache策略：
* CacheType.NO_CACHE：不缓存任何数据，每次都会从python端读取数据
* CacheType.CACHE_PASS_IN_MEM：第一个pass会从python端读取数据，剩下的pass会直接从内存里
读取数据。
注意事项
--------
可能的内存泄露问题
++++++++++++++++++
PaddlePaddle将train.list中的每一行都传递给process函数，从而生成多个generator。当训练数据非常多时，就会生成非常多的generator。
虽然每个generator在没有调用的时候，是几乎不占内存的；但当调用过一次后，generator便会存下当前的上下文(Context)，而这个Context可能会非常大。并且，generator至少需要调用两次才会知道是否停止。所以，即使process函数里面只有一个yield，也需要两次随机选择到相同generator的时候，才会释放该段内存。
.. code-block:: python
def func():
yield 0
f = func() # 创建generator
tmp = next(f) # 调用一次，返回0
tmp = next(f) # 调用第二次的时候，才会Stop Iteration
由于顺序调用这些generator不会出现上述问题，因此有两种解决方案：
1. **最佳推荐**：将样本的地址放入另一个文本文件，train.list写入那个文本文件的地址。即不要将每一个样本都放入train.list。
2. 在generator的上下文中尽量留下非常少的变量引用，例如
.. code-block:: python
def real_process(fn):
# ... read from fn
return result # 当函数返回的时候，python可以解除掉内部变量的引用。
def process(fn):
yield real_process(fn)
注意：这个问题是PyDataProvider读数据时候的逻辑问题，很难整体修正。
内存不够用的情况
++++++++++++++++
PyDataProvider2会尽可能多的使用内存。因此，对于内存较小的机器，推荐使用 ``pool_size`` 变量来设置内存中暂存的数据条。具体请参考 `@provider`_ 中的说明。
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浏览文件 @ d1b6895e
API中文手册
============
DataProvider API
----------------
.. toctree::
:maxdepth: 1
data_provider/dataprovider_cn.rst
data_provider/pydataprovider2_cn.rst
.. _api_trainer_config:
Model Config API
----------------
.. toctree::
:maxdepth: 1
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_cn.rst
develop/doc_cn/_sources/api/v1/predict/swig_py_paddle_cn.rst.txt 已删除 100644 → 0
浏览文件 @ d1b6895e
.. _api_swig_py_paddle:
基于Python的预测
================
预测流程
--------
PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会生成py_paddle软件包，安装该软件包就可以在python环境下实现模型预测。可以使用python的 ``help()`` 函数查询软件包相关API说明。
基于Python的模型预测，主要包括以下五个步骤。
1. 初始化PaddlePaddle环境
在程序开始阶段，通过调用 ``swig_paddle.initPaddle()`` 并传入相应的命令行参数初始化PaddlePaddle。
2. 解析模型配置文件
初始化之后，可以通过调用 ``parse_config()`` 解析训练模型时用的配置文件。注意预测数据通常不包含label, 同时预测网络通常直接输出最后一层的结果而不是像训练网络一样再接一层cost layer，所以一般需要对训练用的模型配置文件稍作相应修改才能在预测时使用。
3. 构造paddle.GradientMachine
通过调用 ``swig_paddle.GradientMachine.createFromConfigproto()`` 传入上一步解析出来的模型配置就可以创建一个 ``GradientMachine``。
4. 准备预测数据
swig_paddle中的预测接口的参数是自定义的C++数据类型，py_paddle里面提供了一个工具类 ``DataProviderConverter`` 可以用于接收和PyDataProvider2一样的输入数据并转换成预测接口所需的数据类型。
5. 模型预测
通过调用 ``forwardTest()`` 传入预测数据，直接返回计算结果。
预测Demo
--------
如下是一段使用mnist model来实现手写识别的预测代码。完整的代码见 ``src_root/doc/ui/predict/predict_sample.py`` 。mnist model可以通过 ``src_root\demo\mnist`` 目录下的demo训练出来。
.. literalinclude:: src/predict_sample.py
:language: python
:lines: 15-18,121-136
Demo预测输出如下，其中value即为softmax层的输出。由于TEST_DATA包含两条预测数据，所以输出的value包含两个向量 。
.. code-block:: text
[{'id': None, 'value': array(
[[ 5.53018653e-09, 1.12194102e-05, 1.96644767e-09,
1.43630644e-02, 1.51111044e-13, 9.85625684e-01,
2.08823112e-10, 2.32777140e-08, 2.00186201e-09,
1.15501715e-08],
[ 9.99982715e-01, 1.27787406e-10, 1.72296313e-05,
1.49316648e-09, 1.36540484e-11, 6.93137714e-10,
2.70634608e-08, 3.48565123e-08, 5.25639710e-09,
4.48684503e-08]], dtype=float32)}]
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<span id="api-dataprovider"></span><h1>DataProvider的介绍<a class="headerlink" href="#dataprovider" title="永久链接至标题"></a></h1>
<p>DataProvider是PaddlePaddle负责提供数据的模块。其作用是将数据传入内存或显存，让神经网络可以进行训练或预测。用户可以通过简单使用Python接口 <a class="reference internal" href="pydataprovider2_cn.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2的使用</span></a> ，来自定义传数据的过程。如果有更复杂的使用，或者需要更高的效率，用户也可以在C++端自定义一个 <code class="docutils literal"><span class="pre">DataProvider</span></code> 。</p>
<p>PaddlePaddle需要用户在网络配置（trainer_config.py）中定义使用哪种DataProvider，并且在DataProvider中实现如何访问训练文件列表（train.list）或测试文件列表（test.list）。</p>
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<li>train.list和test.list存放在本地（推荐直接存放到训练目录，以相对路径引用)。一般情况下，两者均为纯文本文件，其中每一行对应一个数据文件地址：<ul>
<li>如果数据文件存于本地磁盘，这个地址则为它的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)。</li>
<li>地址也可以为hdfs文件路径，或者数据库连接路径等。</li>
<li>由于这个地址会被DataProvider使用，因此，如何解析该地址也是用户自定义DataProvider时需要考虑的地方。</li>
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<li>如果没有设置test.list，或设置为None，那么在训练过程中不会执行测试操作；否则，会根据命令行参数指定的测试方式，在训练过程中进行测试，从而防止过拟合。</li>
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...@@ -304,7 +304,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:</p> ...@@ -304,7 +304,7 @@ PaddlePaddle的内存占用主要分为如下几个方面:</p>
<span class="k">yield</span> <span class="n">get_sample_from_line</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="k">yield</span> <span class="n">get_sample_from_line</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
<p>这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 <a class="reference internal" href="../../api/v1/data_provider/pydataprovider2_cn.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2的使用</span></a> 。</p> <p>这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 <span class="xref std std-ref">api_pydataprovider2</span> 。</p>
</div> </div>
<div class="section" id="id3"> <div class="section" id="id3">
<h3><a class="toc-backref" href="#id13">神经元激活内存</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h3> <h3><a class="toc-backref" href="#id13">神经元激活内存</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h3>
......
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