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############
基本使用概念
############
PaddlePaddle是源于百度的一个深度学习平台。PaddlePaddle为深度学习研究人员提供了丰富的API，可以轻松地完成神经网络配置，模型训练等任务。
这里将介绍PaddlePaddle的基本使用概念，并且展示了如何利用PaddlePaddle来解决一个经典的线性回归问题。
在使用该文档之前，请参考 `安装文档 <../build_and_install/index_cn.html>`_ 完成PaddlePaddle的安装。
配置网络
============
加载PaddlePaddle
----------------------
在进行网络配置之前，首先需要加载相应的Python库，并进行初始化操作。
.. code-block:: bash
import paddle.v2 as paddle
import numpy as np
paddle.init(use_gpu=False)
搭建神经网络
-----------------------
搭建神经网络就像使用积木搭建宝塔一样。在PaddlePaddle中，layer是我们的积木，而神经网络是我们要搭建的宝塔。我们使用不同的layer进行组合，来搭建神经网络。
宝塔的底端需要坚实的基座来支撑，同样，神经网络也需要一些特定的layer作为输入接口，来完成网络的训练。
例如，我们可以定义如下layer来描述神经网络的输入：
.. code-block:: bash
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
其中x表示输入数据是一个维度为2的稠密向量，y表示输入数据是一个维度为1的稠密向量。
PaddlePaddle支持不同类型的输入数据，主要包括四种类型，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)]`` 。
在定义输入layer之后，我们可以使用其他layer进行组合。在组合时，需要指定layer的输入来源。
例如，我们可以定义如下的layer组合：
.. code-block:: bash
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
cost = paddle.layer.mse_cost(input=y_predict, label=y)
其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。
最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。
训练模型
============
在完成神经网络的搭建之后，我们首先需要根据神经网络结构来创建所需要优化的parameters，并创建optimizer。
之后，我们可以创建trainer来对网络进行训练。
.. code-block:: bash
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(momentum=0)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer)
其中，trainer接收三个参数，包括神经网络拓扑结构、神经网络参数以及迭代方程。
在搭建神经网络的过程中，我们仅仅对神经网络的输入进行了描述。而trainer需要读取训练数据进行训练，PaddlePaddle中通过reader来加载数据。
.. code-block:: bash
# define training dataset reader
def train_reader():
train_x = np.array([[1, 1], [1, 2], [3, 4], [5, 2]])
train_y = np.array([-2, -3, -7, -7])
def reader():
for i in xrange(train_y.shape[0]):
yield train_x[i], train_y[i]
return reader
最终我们可以调用trainer的train方法启动训练：
.. code-block:: bash
# define feeding map
feeding = {'x': 0, 'y': 1}
# event_handler to print training info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
# training
trainer.train(
reader=paddle.batch(train_reader(), batch_size=1),
feeding=feeding,
event_handler=event_handler,
num_passes=100)
关于PaddlePaddle的更多使用方法请参考 `进阶指南 <../../howto/index_cn.html>`_。
线性回归完整示例
==============
下面给出在三维空间中使用线性回归拟合一条直线的例子：
.. literalinclude:: src/train.py
:linenos:
有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。
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:maxdepth: 1 :maxdepth: 1
build_and_install/index_cn.rst build_and_install/index_cn.rst
concepts/use_concepts_cn.rst
- `深度学习入门课程 <http://book.paddlepaddle.org/>`_ - `深度学习入门课程 <http://book.paddlepaddle.org/>`_
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usage/concepts/use_concepts_cn.rst
usage/cluster/cluster_train_cn.md usage/cluster/cluster_train_cn.md
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############
基本使用概念
############
PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。
单机模式用命令 ``paddle train`` 可以启动一个trainer进程，单机训练通常只包括一个trainer进程。如果数据规模比较大，希望加速训练，可以启动分布式作业。一个分布式作业里包括若干trainer进程和若干Parameter Server（或称pserver）进程。用命令 ``paddle pserver`` 可以启动 pserver 进程，pserver进程用于协调多个trainer进程之间的通信。
本文首先介绍trainer进程中的一些使用概念，然后介绍pserver进程中概念。
.. contents::
系统框图
========
下图描述了用户使用框图，PaddlePaddle的trainer进程里内嵌了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为数据提供器（DataProvider），通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中, 称为训练配置文件。下面将分别介绍这两部分。
.. graphviz::
digraph pp_process {
rankdir=LR;
config_file [label="用户神经网络配置"];
subgraph cluster_pp {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = "PaddlePaddle C++";
py [label="Python解释器"];
}
data_provider [label="用户数据解析"];
config_file -> py;
py -> data_provider [dir="back"];
}
数据提供器
==========
DataProvider是PaddlePaddle系统的数据提供器，将用户的原始数据转换成系统可以识别的数据类型。每当系统需要新的数据训练时, trainer进程会调用DataProvider函数返回数据。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，并且系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。
在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 ``PyDataProvider`` 。在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：``min_pool_size=0``)需要Python接口里处理shuffle，可以参考 :ref:`api_pydataprovider2` 继续深入了解。
训练配置文件
============
训练配置文件主要包括数据源、优化算法、网络结构配置三部分。 其中数据源配置与DataProvider的关系是：DataProvider里定义数据读取函数，训练配置文件的数据源配置中指定DataProvider文件名字、生成数据函数接口，请不要混淆。
一个简单的训练配置文件为：
.. literalinclude:: src/trainer_config.py
:linenos:
文件开头 ``from paddle.trainer_config_helpers import *`` ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf，为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是 :ref:`api_trainer_config` 的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。
下面分别介绍数据源配置、优化算法配置、网络结构配置这三部分该概念。
数据源配置
----------
使用 ``PyDataProvider2`` 的函数 ``define_py_data_sources2`` 配置数据源。``define_py_data_sources2`` 里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。
``module`` 和 ``obj`` 指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 :ref:`api_pydataprovider2` 。
优化算法配置
------------
通过 :ref:`api_trainer_config_helpers_optimizers_settings` 接口设置神经网络所使用的训练参数和 :ref:`api_trainer_config_helpers_optimizers` ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 :ref:`api_trainer_config_helpers_optimizers_settings` 文档。
网络结构配置
------------
神经网络配置主要包括网络连接、激活函数、损失函数、评估器。
- 网络连接： 主要由Layer组成，每个Layer返回的都是一个 ``LayerOutput`` 对象，Layer里面可以定义参数属性、激活类型等。
为了更灵活的配置，PaddlePaddle提供了基于 Projection 或者 Operator 的配置，这两个需要与 ``mixed_layer`` 配合使用。这里简单介绍Layer、Projection、Operator的概念:
- Layer: 神经网络的某一层，可以有可学习的参数，一般是封装了许多复杂操作的集合。
- Projection：需要与 ``mixed_layer`` 配合使用，含可学习参数。
- Operator： 需要与 ``mixed_layer`` 配合使用，不含可学习参数，输入全是其他Layer的输出。
这个配置文件网络由 ``data_layer`` 、 ``simple_img_conv_pool`` 、 ``fc_layer`` 组成。
- :ref:`api_trainer_config_helpers_layers_data_layer` ： 通常每个配置文件都会包括 ``data_layer`` ，定义输入数据大小。
- :ref:`api_trainer_config_helpers_network_simple_img_conv_pool` ：是一个组合层，包括了图像的卷积 (convolution)和池化(pooling)。
- :ref:`api_trainer_config_helpers_layers_fc_layer` ：全连接层，激活函数为Softmax，这里也可叫分类层。
- 损失函数和评估器：损失函数即为网络的优化目标，评估器可以评价模型结果。
PaddlePaddle包括很多损失函数和评估起，详细可以参考 :ref:`api_trainer_config_helpers_layers_cost_layers` 和 :ref:`api_trainer_config_helpers_evaluators` 。这里 ``classification_cost`` 默认使用多类交叉熵损失函数和分类错误率统计评估器。
- ``outputs``: 标记网络输出的函数为 ``outputs`` 。
训练阶段，网络的输出为神经网络的优化目标；预测阶段，网络的输出也可通过 ``outputs`` 标记。
这里对 ``mixed_layer`` 稍做详细说明， 该Layer将多个输入(Projection 或 Operator)累加求和，具体计算是通过内部的 Projection 和 Operator 完成，然后加 Bias 和 activation 操作，
例如，和 ``fc_layer`` 同样功能的 ``mixed_layer`` 是:
.. code-block:: python
data = data_layer(name='data', size=200)
with mixed_layer(size=200) as out:
out += full_matrix_projection(input=data)
PaddlePaddle 可以使用 ``mixed layer`` 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 :ref:`api_trainer_config_helpers_layers_mixed_layer` 的相关文档进行配置。
分布式训练
==========
PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trainer 进行同步。多机训练的经典拓扑结构如下\:
.. graphviz:: src/pserver_topology.dot
图中每个灰色方块是一台机器，在每个机器中，先使用命令 ``paddle pserver`` 启动一个pserver进程，并指定端口号，可能的参数是\:
.. code-block:: bash
paddle pserver --port=5000 --num_gradient_servers=4 --tcp_rdma='tcp' --nics='eth0'
* ``--port=5000`` : 指定 pserver 进程端口是 5000 。
* ``--gradient_servers=4`` : 有四个训练进程(PaddlePaddle 将 trainer 也称作 GradientServer ，因为其为负责提供Gradient) 。
* ``--tcp_rdma='tcp' --nics=`eth0```: 指定以太网类型为TCP网络，指定网络接口名字为eth0。
启动之后 pserver 进程之后，需要启动 trainer 训练进程，在各个机器上运行如下命令\:
.. code-block:: bash
paddle train --port=5000 --pservers=192.168.100.101,192.168.100.102,192.168.100.103,192.168.100.104 --config=...
对于简单的多机协同训练使用上述方式即可。另外，pserver/train 通常在高级情况下，还需要设置下面两个参数\：
* --ports_num\: 一个 pserver 进程共绑定多少个端口用来做稠密更新，默认是1。
* --ports_num_for_sparse\: 一个pserver进程共绑定多少端口用来做稀疏更新，默认是0。
使用手工指定端口数量，是因为Paddle的网络通信中，使用了 int32 作为消息长度，比较容易在大模型下溢出。所以，在 pserver 进程中可以启动多个子线程去接受 trainer 的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。
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<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li> <li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul> <li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li> <li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/concepts/use_concepts_cn.html">基本使用概念</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li> <li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li> <li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul> <li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li> <li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/concepts/use_concepts_cn.html">基本使用概念</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li> <li class="toctree-l2"><a class="reference internal" href="../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li> <li class="toctree-l2"><a class="reference internal" href="../../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
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<h1>基本使用概念<a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>PaddlePaddle是源于百度的一个深度学习平台。PaddlePaddle为深度学习研究人员提供了丰富的API，可以轻松地完成神经网络配置，模型训练等任务。
这里将介绍PaddlePaddle的基本使用概念，并且展示了如何利用PaddlePaddle来解决一个经典的线性回归问题。
在使用该文档之前，请参考 <a class="reference external" href="../build_and_install/index_cn.html">安装文档</a> 完成PaddlePaddle的安装。</p>
<div class="section" id="id3">
<h2>配置网络<a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<div class="section" id="paddlepaddle">
<h3>加载PaddlePaddle<a class="headerlink" href="#paddlepaddle" title="永久链接至标题"></a></h3>
<p>在进行网络配置之前，首先需要加载相应的Python库，并进行初始化操作。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>import paddle.v2 as paddle
import numpy as np
paddle.init<span class="o">(</span><span class="nv">use_gpu</span><span class="o">=</span>False<span class="o">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h3>搭建神经网络<a class="headerlink" href="#id4" title="永久链接至标题"></a></h3>
<p>搭建神经网络就像使用积木搭建宝塔一样。在PaddlePaddle中，layer是我们的积木，而神经网络是我们要搭建的宝塔。我们使用不同的layer进行组合，来搭建神经网络。
宝塔的底端需要坚实的基座来支撑，同样，神经网络也需要一些特定的layer作为输入接口，来完成网络的训练。</p>
<p>例如，我们可以定义如下layer来描述神经网络的输入：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">x</span> <span class="o">=</span> paddle.layer.data<span class="o">(</span><span class="nv">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span>, <span class="nv">type</span><span class="o">=</span>paddle.data_type.dense_vector<span class="o">(</span><span class="m">2</span><span class="o">))</span>
<span class="nv">y</span> <span class="o">=</span> paddle.layer.data<span class="o">(</span><span class="nv">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span>, <span class="nv">type</span><span class="o">=</span>paddle.data_type.dense_vector<span class="o">(</span><span class="m">1</span><span class="o">))</span>
</pre></div>
</div>
<p>其中x表示输入数据是一个维度为2的稠密向量，y表示输入数据是一个维度为1的稠密向量。</p>
<p>PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和三种序列模式。</p>
<p>四种数据类型：</p>
<ul class="simple">
<li>dense_vector：稠密的浮点数向量。</li>
<li>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。</li>
<li>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。</li>
<li>integer：整数标签。</li>
</ul>
<p>三种序列模式：</p>
<ul class="simple">
<li>SequenceType.NO_SEQUENCE：不是一条序列</li>
<li>SequenceType.SEQUENCE：是一条时间序列</li>
<li>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。</li>
</ul>
<p>不同的数据类型和序列模式返回的格式不同，列表如下：</p>
<table border="1" class="docutils">
<colgroup>
<col width="17%" />
<col width="17%" />
<col width="28%" />
<col width="38%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">&#160;</th>
<th class="head">NO_SEQUENCE</th>
<th class="head">SEQUENCE</th>
<th class="head">SUB_SEQUENCE</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>dense_vector</td>
<td>[f, f, ...]</td>
<td>[[f, ...], [f, ...], ...]</td>
<td>[[[f, ...], ...], [[f, ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>sparse_binary_vector</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
<td>[[[i, ...], ...], [[i, ...], ...],...]</td>
</tr>
<tr class="row-even"><td>sparse_float_vector</td>
<td>[(i,f), (i,f), ...]</td>
<td>[[(i,f), ...], [(i,f), ...], ...]</td>
<td>[[[(i,f), ...], ...], [[(i,f), ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>integer_value</td>
<td>i</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
</tr>
</tbody>
</table>
<p>其中，f代表一个浮点数，i代表一个整数。</p>
<p>注意：对sparse_binary_vector和sparse_float_vector，PaddlePaddle存的是有值位置的索引。例如，</p>
<ul class="simple">
<li>对一个5维非序列的稀疏01向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">1,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_binary_vector，返回的是 <code class="docutils literal"><span class="pre">[1,</span> <span class="pre">2]</span></code> 。</li>
<li>对一个5维非序列的稀疏浮点向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">0.5,</span> <span class="pre">0.7,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_float_vector，返回的是 <code class="docutils literal"><span class="pre">[(1,</span> <span class="pre">0.5),</span> <span class="pre">(2,</span> <span class="pre">0.7)]</span></code> 。</li>
</ul>
<p>在定义输入layer之后，我们可以使用其他layer进行组合。在组合时，需要指定layer的输入来源。</p>
<p>例如，我们可以定义如下的layer组合：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">y_predict</span> <span class="o">=</span> paddle.layer.fc<span class="o">(</span><span class="nv">input</span><span class="o">=</span>x, <span class="nv">size</span><span class="o">=</span><span class="m">1</span>, <span class="nv">act</span><span class="o">=</span>paddle.activation.Linear<span class="o">())</span>
<span class="nv">cost</span> <span class="o">=</span> paddle.layer.mse_cost<span class="o">(</span><span class="nv">input</span><span class="o">=</span>y_predict, <span class="nv">label</span><span class="o">=</span>y<span class="o">)</span>
</pre></div>
</div>
<p>其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。</p>
<p>最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。</p>
</div>
</div>
<div class="section" id="id5">
<h2>训练模型<a class="headerlink" href="#id5" title="永久链接至标题"></a></h2>
<p>在完成神经网络的搭建之后，我们首先需要根据神经网络结构来创建所需要优化的parameters，并创建optimizer。
之后，我们可以创建trainer来对网络进行训练。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">parameters</span> <span class="o">=</span> paddle.parameters.create<span class="o">(</span>cost<span class="o">)</span>
<span class="nv">optimizer</span> <span class="o">=</span> paddle.optimizer.Momentum<span class="o">(</span><span class="nv">momentum</span><span class="o">=</span><span class="m">0</span><span class="o">)</span>
<span class="nv">trainer</span> <span class="o">=</span> paddle.trainer.SGD<span class="o">(</span><span class="nv">cost</span><span class="o">=</span>cost,
<span class="nv">parameters</span><span class="o">=</span>parameters,
<span class="nv">update_equation</span><span class="o">=</span>optimizer<span class="o">)</span>
</pre></div>
</div>
<p>其中，trainer接收三个参数，包括神经网络拓扑结构、神经网络参数以及迭代方程。</p>
<p>在搭建神经网络的过程中，我们仅仅对神经网络的输入进行了描述。而trainer需要读取训练数据进行训练，PaddlePaddle中通过reader来加载数据。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># define training dataset reader</span>
def train_reader<span class="o">()</span>:
<span class="nv">train_x</span> <span class="o">=</span> np.array<span class="o">([[</span><span class="m">1</span>, <span class="m">1</span><span class="o">]</span>, <span class="o">[</span><span class="m">1</span>, <span class="m">2</span><span class="o">]</span>, <span class="o">[</span><span class="m">3</span>, <span class="m">4</span><span class="o">]</span>, <span class="o">[</span><span class="m">5</span>, <span class="m">2</span><span class="o">]])</span>
<span class="nv">train_y</span> <span class="o">=</span> np.array<span class="o">([</span>-2, -3, -7, -7<span class="o">])</span>
def reader<span class="o">()</span>:
<span class="k">for</span> i in xrange<span class="o">(</span>train_y.shape<span class="o">[</span><span class="m">0</span><span class="o">])</span>:
yield train_x<span class="o">[</span>i<span class="o">]</span>, train_y<span class="o">[</span>i<span class="o">]</span>
<span class="k">return</span> reader
</pre></div>
</div>
<p>最终我们可以调用trainer的train方法启动训练：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># define feeding map</span>
<span class="nv">feeding</span> <span class="o">=</span> <span class="o">{</span><span class="s1">&#39;x&#39;</span>: <span class="m">0</span>, <span class="s1">&#39;y&#39;</span>: <span class="m">1</span><span class="o">}</span>
<span class="c1"># event_handler to print training info</span>
def event_handler<span class="o">(</span>event<span class="o">)</span>:
<span class="k">if</span> isinstance<span class="o">(</span>event, paddle.event.EndIteration<span class="o">)</span>:
<span class="k">if</span> event.batch_id % <span class="nv">1</span> <span class="o">==</span> <span class="m">0</span>:
print <span class="s2">&quot;Pass %d, Batch %d, Cost %f&quot;</span> % <span class="o">(</span>
event.pass_id, event.batch_id, event.cost<span class="o">)</span>
<span class="c1"># training</span>
trainer.train<span class="o">(</span>
<span class="nv">reader</span><span class="o">=</span>paddle.batch<span class="o">(</span>train_reader<span class="o">()</span>, <span class="nv">batch_size</span><span class="o">=</span><span class="m">1</span><span class="o">)</span>,
<span class="nv">feeding</span><span class="o">=</span>feeding,
<span class="nv">event_handler</span><span class="o">=</span>event_handler,
<span class="nv">num_passes</span><span class="o">=</span><span class="m">100</span><span class="o">)</span>
</pre></div>
</div>
<p>关于PaddlePaddle的更多使用方法请参考 <a class="reference external" href="../../howto/index_cn.html">进阶指南</a>。</p>
</div>
<div class="section" id="id7">
<h2>线性回归完整示例<a class="headerlink" href="#id7" title="永久链接至标题"></a></h2>
<p>下面给出在三维空间中使用线性回归拟合一条直线的例子：</p>
<div class="highlight-default"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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52</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="k">as</span> <span class="nn">paddle</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># init paddle</span>
<span class="n">paddle</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">use_gpu</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># network config</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">y_predict</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Linear</span><span class="p">())</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="c1"># create parameters</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
<span class="c1"># create optimizer</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Momentum</span><span class="p">(</span><span class="n">momentum</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># create trainer</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="n">cost</span><span class="p">,</span>
<span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span>
<span class="n">update_equation</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>
<span class="c1"># event_handler to print training info</span>
<span class="k">def</span> <span class="nf">event_handler</span><span class="p">(</span><span class="n">event</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">EndIteration</span><span class="p">):</span>
<span class="k">if</span> <span class="n">event</span><span class="o">.</span><span class="n">batch_id</span> <span class="o">%</span> <span class="mi">1</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span> <span class="s2">&quot;Pass </span><span class="si">%d</span><span class="s2">, Batch </span><span class="si">%d</span><span class="s2">, Cost </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">pass_id</span><span class="p">,</span> <span class="n">event</span><span class="o">.</span><span class="n">batch_id</span><span class="p">,</span>
<span class="n">event</span><span class="o">.</span><span class="n">cost</span><span class="p">)</span>
<span class="c1"># define training dataset reader</span>
<span class="k">def</span> <span class="nf">train_reader</span><span class="p">():</span>
<span class="n">train_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
<span class="n">train_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">7</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">reader</span><span class="p">():</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">xrange</span><span class="p">(</span><span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="k">yield</span> <span class="n">train_x</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">train_y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">reader</span>
<span class="c1"># define feeding map</span>
<span class="n">feeding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
<span class="c1"># training</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
<span class="n">reader</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
<span class="n">train_reader</span><span class="p">(),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">,</span>
<span class="n">event_handler</span><span class="o">=</span><span class="n">event_handler</span><span class="p">,</span>
<span class="n">num_passes</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
</pre></div>
</td></tr></table></div>
<p>有关线性回归的实际应用，可以参考PaddlePaddle book的 <a class="reference external" href="http://book.paddlepaddle.org/index.html">第一章节</a>。</p>
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<h1><a class="toc-backref" href="#id9">基本使用概念</a><a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。</p>
<p>单机模式用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">train</span></code> 可以启动一个trainer进程，单机训练通常只包括一个trainer进程。如果数据规模比较大，希望加速训练，可以启动分布式作业。一个分布式作业里包括若干trainer进程和若干Parameter Server（或称pserver）进程。用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">pserver</span></code> 可以启动 pserver 进程，pserver进程用于协调多个trainer进程之间的通信。</p>
<p>本文首先介绍trainer进程中的一些使用概念，然后介绍pserver进程中概念。</p>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#id1" id="id9">基本使用概念</a><ul>
<li><a class="reference internal" href="#id2" id="id10">系统框图</a></li>
<li><a class="reference internal" href="#id3" id="id11">数据提供器</a></li>
<li><a class="reference internal" href="#id4" id="id12">训练配置文件</a><ul>
<li><a class="reference internal" href="#id5" id="id13">数据源配置</a></li>
<li><a class="reference internal" href="#id6" id="id14">优化算法配置</a></li>
<li><a class="reference internal" href="#id7" id="id15">网络结构配置</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id8" id="id16">分布式训练</a></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="id2">
<h2><a class="toc-backref" href="#id10">系统框图</a><a class="headerlink" href="#id2" title="永久链接至标题"></a></h2>
<p>下图描述了用户使用框图，PaddlePaddle的trainer进程里内嵌了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为数据提供器（DataProvider），通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中, 称为训练配置文件。下面将分别介绍这两部分。</p>
<img src="../../../_images/graphviz-8d00840e833ead7ea6247faeb79235bf4bdfd442.png" alt="digraph pp_process {
rankdir=LR;
config_file [label=&quot;用户神经网络配置&quot;];
subgraph cluster_pp {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = &quot;PaddlePaddle C++&quot;;
py [label=&quot;Python解释器&quot;];
}
data_provider [label=&quot;用户数据解析&quot;];
config_file -&gt; py;
py -&gt; data_provider [dir=&quot;back&quot;];
}" />
</div>
<div class="section" id="id3">
<h2><a class="toc-backref" href="#id11">数据提供器</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<p>DataProvider是PaddlePaddle系统的数据提供器，将用户的原始数据转换成系统可以识别的数据类型。每当系统需要新的数据训练时, trainer进程会调用DataProvider函数返回数据。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，并且系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。</p>
<p>在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 <code class="docutils literal"><span class="pre">PyDataProvider</span></code> 。在 <code class="docutils literal"><span class="pre">PyDataProvider</span></code> 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：<code class="docutils literal"><span class="pre">min_pool_size=0</span></code>)需要Python接口里处理shuffle，可以参考 <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> 继续深入了解。</p>
</div>
<div class="section" id="id4">
<h2><a class="toc-backref" href="#id12">训练配置文件</a><a class="headerlink" href="#id4" title="永久链接至标题"></a></h2>
<p>训练配置文件主要包括数据源、优化算法、网络结构配置三部分。 其中数据源配置与DataProvider的关系是：DataProvider里定义数据读取函数，训练配置文件的数据源配置中指定DataProvider文件名字、生成数据函数接口，请不要混淆。</p>
<p>一个简单的训练配置文件为：</p>
<div class="highlight-default"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer_config_helpers</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">define_py_data_sources2</span><span class="p">(</span>
<span class="n">train_list</span><span class="o">=</span><span class="s1">&#39;train.list&#39;</span><span class="p">,</span>
<span class="n">test_list</span><span class="o">=</span><span class="s1">&#39;test.list&#39;</span><span class="p">,</span>
<span class="n">module</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>
<span class="n">obj</span><span class="o">=</span><span class="s1">&#39;process&#39;</span><span class="p">)</span>
<span class="n">settings</span><span class="p">(</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
<span class="n">learning_method</span><span class="o">=</span><span class="n">AdamOptimizer</span><span class="p">(),</span>
<span class="n">regularization</span><span class="o">=</span><span class="n">L2Regularization</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;pixel&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>
<span class="n">hidden1</span> <span class="o">=</span> <span class="n">simple_img_conv_pool</span><span class="p">(</span>
<span class="nb">input</span><span class="o">=</span><span class="n">img</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_channel</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">hidden2</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span>
<span class="nb">input</span><span class="o">=</span><span class="n">hidden1</span><span class="p">,</span>
<span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
<span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
<span class="n">layer_attr</span><span class="o">=</span><span class="n">ExtraAttr</span><span class="p">(</span><span class="n">drop_rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">))</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">hidden2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">SoftmaxActivation</span><span class="p">())</span>
<span class="n">outputs</span><span class="p">(</span>
<span class="n">classification_cost</span><span class="p">(</span>
<span class="nb">input</span><span class="o">=</span><span class="n">predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)))</span>
</pre></div>
</td></tr></table></div>
<p>文件开头 <code class="docutils literal"><span class="pre">from</span> <span class="pre">paddle.trainer_config_helpers</span> <span class="pre">import</span> <span class="pre">*</span></code> ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf，为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是 <a class="reference internal" href="../../../api/v1/index_cn.html#api-trainer-config"><span class="std std-ref">Model Config API</span></a> 的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。</p>
<p>下面分别介绍数据源配置、优化算法配置、网络结构配置这三部分该概念。</p>
<div class="section" id="id5">
<h3><a class="toc-backref" href="#id13">数据源配置</a><a class="headerlink" href="#id5" title="永久链接至标题"></a></h3>
<p>使用 <code class="docutils literal"><span class="pre">PyDataProvider2</span></code> 的函数 <code class="docutils literal"><span class="pre">define_py_data_sources2</span></code> 配置数据源。<code class="docutils literal"><span class="pre">define_py_data_sources2</span></code> 里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。</p>
<p><code class="docutils literal"><span class="pre">module</span></code> 和 <code class="docutils literal"><span class="pre">obj</span></code> 指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 <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>
</div>
<div class="section" id="id6">
<h3><a class="toc-backref" href="#id14">优化算法配置</a><a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>通过 <span class="xref std std-ref">api_trainer_config_helpers_optimizers_settings</span> 接口设置神经网络所使用的训练参数和 <span class="xref std std-ref">api_trainer_config_helpers_optimizers</span> ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 <span class="xref std std-ref">api_trainer_config_helpers_optimizers_settings</span> 文档。</p>
</div>
<div class="section" id="id7">
<h3><a class="toc-backref" href="#id15">网络结构配置</a><a class="headerlink" href="#id7" title="永久链接至标题"></a></h3>
<p>神经网络配置主要包括网络连接、激活函数、损失函数、评估器。</p>
<ul>
<li><p class="first">网络连接： 主要由Layer组成，每个Layer返回的都是一个 <code class="docutils literal"><span class="pre">LayerOutput</span></code> 对象，Layer里面可以定义参数属性、激活类型等。</p>
<p>为了更灵活的配置，PaddlePaddle提供了基于 Projection 或者 Operator 的配置，这两个需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用。这里简单介绍Layer、Projection、Operator的概念:</p>
<ul class="simple">
<li>Layer: 神经网络的某一层，可以有可学习的参数，一般是封装了许多复杂操作的集合。</li>
<li>Projection：需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用，含可学习参数。</li>
<li>Operator： 需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用，不含可学习参数，输入全是其他Layer的输出。</li>
</ul>
<p>这个配置文件网络由 <code class="docutils literal"><span class="pre">data_layer</span></code> 、 <code class="docutils literal"><span class="pre">simple_img_conv_pool</span></code> 、 <code class="docutils literal"><span class="pre">fc_layer</span></code> 组成。</p>
<ul class="simple">
<li><span class="xref std std-ref">api_trainer_config_helpers_layers_data_layer</span> ： 通常每个配置文件都会包括 <code class="docutils literal"><span class="pre">data_layer</span></code> ，定义输入数据大小。</li>
<li><a class="reference internal" href="../../../api/v2/config/networks.html#api-trainer-config-helpers-network-simple-img-conv-pool"><span class="std std-ref">simple_img_conv_pool</span></a> ：是一个组合层，包括了图像的卷积 (convolution)和池化(pooling)。</li>
<li><span class="xref std std-ref">api_trainer_config_helpers_layers_fc_layer</span> ：全连接层，激活函数为Softmax，这里也可叫分类层。</li>
</ul>
</li>
<li><p class="first">损失函数和评估器：损失函数即为网络的优化目标，评估器可以评价模型结果。</p>
<p>PaddlePaddle包括很多损失函数和评估起，详细可以参考 <span class="xref std std-ref">api_trainer_config_helpers_layers_cost_layers</span> 和 <span class="xref std std-ref">api_trainer_config_helpers_evaluators</span> 。这里 <code class="docutils literal"><span class="pre">classification_cost</span></code> 默认使用多类交叉熵损失函数和分类错误率统计评估器。</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">outputs</span></code>: 标记网络输出的函数为 <code class="docutils literal"><span class="pre">outputs</span></code> 。</p>
<p>训练阶段，网络的输出为神经网络的优化目标；预测阶段，网络的输出也可通过 <code class="docutils literal"><span class="pre">outputs</span></code> 标记。</p>
</li>
</ul>
<p>这里对 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 稍做详细说明， 该Layer将多个输入(Projection 或 Operator)累加求和，具体计算是通过内部的 Projection 和 Operator 完成，然后加 Bias 和 activation 操作，</p>
<p>例如，和 <code class="docutils literal"><span class="pre">fc_layer</span></code> 同样功能的 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 是:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span> <span class="k">as</span> <span class="n">out</span><span class="p">:</span>
<span class="n">out</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
<p>PaddlePaddle 可以使用 <code class="docutils literal"><span class="pre">mixed</span> <span class="pre">layer</span></code> 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 <span class="xref std std-ref">api_trainer_config_helpers_layers_mixed_layer</span> 的相关文档进行配置。</p>
</div>
</div>
<div class="section" id="id8">
<h2><a class="toc-backref" href="#id16">分布式训练</a><a class="headerlink" href="#id8" title="永久链接至标题"></a></h2>
<p>PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trainer 进行同步。多机训练的经典拓扑结构如下:</p>
<img src="../../../_images/graphviz-e02b084d1b1b525450b262148a6b8c5f2a2c3c68.png" alt="graph pp_topology {
rankdir=BT;
subgraph cluster_node0 {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = &quot;机器0&quot;
pserver0 [label=&quot;Parameter \n Server 0&quot;]
trainer0 [label=&quot;Trainer 0&quot;]
}
subgraph cluster_node1 {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = &quot;机器1&quot;
pserver1 [label=&quot;Parameter \n Server 1&quot;]
trainer1 [label=&quot;Trainer 1&quot;]
}
subgraph cluster_node2 {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = &quot;机器2&quot;
pserver2 [label=&quot;Parameter \n Server 2&quot;]
trainer2 [label=&quot;Trainer 2&quot;]
}
subgraph cluster_node3 {
style=filled;
color=lightgrey;
node [style=filled, color=white, shape=box];
label = &quot;机器3&quot;
pserver3 [label=&quot;Parameter \n Server 3&quot;]
trainer3 [label=&quot;Trainer 3&quot;]
}
data [label=&quot;数据&quot;, shape=hexagon]
trainer0 -- pserver0
trainer0 -- pserver1
trainer0 -- pserver2
trainer0 -- pserver3
trainer1 -- pserver0
trainer1 -- pserver1
trainer1 -- pserver2
trainer1 -- pserver3
trainer2 -- pserver0
trainer2 -- pserver1
trainer2 -- pserver2
trainer2 -- pserver3
trainer3 -- pserver0
trainer3 -- pserver1
trainer3 -- pserver2
trainer3 -- pserver3
data -- trainer0
data -- trainer1
data -- trainer2
data -- trainer3
}" />
<p>图中每个灰色方块是一台机器，在每个机器中，先使用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">pserver</span></code> 启动一个pserver进程，并指定端口号，可能的参数是:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle pserver --port<span class="o">=</span><span class="m">5000</span> --num_gradient_servers<span class="o">=</span><span class="m">4</span> --tcp_rdma<span class="o">=</span><span class="s1">&#39;tcp&#39;</span> --nics<span class="o">=</span><span class="s1">&#39;eth0&#39;</span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">--port=5000</span></code> : 指定 pserver 进程端口是 5000 。</li>
<li><code class="docutils literal"><span class="pre">--gradient_servers=4</span></code> : 有四个训练进程(PaddlePaddle 将 trainer 也称作 GradientServer ，因为其为负责提供Gradient) 。</li>
<li><code class="docutils literal"><span class="pre">--tcp_rdma='tcp'</span> <span class="pre">--nics=`eth0`</span></code>: 指定以太网类型为TCP网络，指定网络接口名字为eth0。</li>
</ul>
<p>启动之后 pserver 进程之后，需要启动 trainer 训练进程，在各个机器上运行如下命令:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle train --port<span class="o">=</span><span class="m">5000</span> --pservers<span class="o">=</span><span class="m">192</span>.168.100.101,192.168.100.102,192.168.100.103,192.168.100.104 --config<span class="o">=</span>...
</pre></div>
</div>
<p>对于简单的多机协同训练使用上述方式即可。另外，pserver/train 通常在高级情况下，还需要设置下面两个参数：</p>
<ul class="simple">
<li>&#8211;ports_num: 一个 pserver 进程共绑定多少个端口用来做稠密更新，默认是1。</li>
<li>&#8211;ports_num_for_sparse: 一个pserver进程共绑定多少端口用来做稀疏更新，默认是0。</li>
</ul>
<p>使用手工指定端口数量，是因为Paddle的网络通信中，使用了 int32 作为消息长度，比较容易在大模型下溢出。所以，在 pserver 进程中可以启动多个子线程去接受 trainer 的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。</p>
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