Deploy to GitHub Pages: ad789814

release/0.10.0/doc_cn/_images/graphviz-8d00840e833ead7ea6247faeb79235bf4bdfd442.png.map

-<map id="pp_process" name="pp_process">
-</map>

release/0.10.0/doc_cn/_images/graphviz-e02b084d1b1b525450b262148a6b8c5f2a2c3c68.png.map

-<map id="pp_topology" name="pp_topology">
-</map>

release/0.10.0/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt

+############
+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>`_。
\ No newline at end of file

release/0.10.0/doc_cn/_sources/getstarted/index_cn.rst.txt

@@ -5,5 +5,6 @@
   :maxdepth: 1
 
   build_and_install/index_cn.rst
+  concepts/use_concepts_cn.rst
 
 - `深度学习入门课程 <http://book.paddlepaddle.org/>`_

release/0.10.0/doc_cn/_sources/howto/index_cn.rst.txt

@@ -8,7 +8,6 @@
   :maxdepth: 1
 
   usage/cmd_parameter/index_cn.rst
-  usage/concepts/use_concepts_cn.rst
   usage/cluster/cluster_train_cn.md
   usage/k8s/k8s_basis_cn.md
   usage/k8s/k8s_cn.md

release/0.10.0/doc_cn/_sources/howto/usage/concepts/use_concepts_cn.rst.txt

-############
-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。

release/0.10.0/doc_cn/about/index_cn.html

@@ -113,6 +113,7 @@
 <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>
 </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>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul>
@@ -122,7 +123,6 @@
 <li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
 </ul>
 </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/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>

release/0.10.0/doc_cn/api/index_cn.html

@@ -115,6 +115,7 @@
 <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>
 </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>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul>
@@ -124,7 +125,6 @@
 <li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
 </ul>
 </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/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>

release/0.10.0/doc_cn/api/v1/data_provider/dataprovider_cn.html

@@ -113,6 +113,7 @@
 <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>
 </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>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../howto/index_cn.html">进阶指南</a><ul>
@@ -122,7 +123,6 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
 </ul>
 </li>
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release/0.10.0/doc_cn/getstarted/build_and_install/index_cn.html

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release/0.10.0/doc_cn/getstarted/build_and_install/ubuntu_install_cn.html

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release/0.10.0/doc_cn/getstarted/concepts/use_concepts_cn.html

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+        <li><a href="../index_cn.html">新手入门</a> > </li>
+      
+    <li>基本使用概念</li>
+  </ul>
+</div>
+      
+      <div class="wy-nav-content" id="doc-content">
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+            
+  <div class="section" id="id1">
+<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
+ 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
+30
+31
+32
+33
+34
+35
+36
+37
+38
+39
+40
+41
+42
+43
+44
+45
+46
+47
+48
+49
+50
+51
+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>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+  
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="../../howto/index_cn.html" class="btn btn-neutral float-right" title="进阶指南" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+      
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+        <a href="../build_and_install/cmake/build_from_source_cn.html" class="btn btn-neutral" title="PaddlePaddle的编译选项" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+      
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+  <div role="contentinfo">
+    <p>
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+        var DOCUMENTATION_OPTIONS = {
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+<li class="toctree-l2"><a class="reference internal" href="concepts/use_concepts_cn.html">基本使用概念</a></li>
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 </li>
+<li class="toctree-l2"><a class="reference internal" href="../../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
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-  <div class="section" id="id1">
-<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 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 接口设置神经网络所使用的训练参数和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers"><span class="std std-ref">Optimizers</span></a> ，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 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 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><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a>  ： 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><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a> ：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 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cost-layers"><span class="std std-ref">Cost Layers</span></a> 和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/evaluators.html#api-trainer-config-helpers-evaluators"><span class="std std-ref">Evaluators</span></a> 。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 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-mixed-layer"><span class="std std-ref">mixed_layer</span></a> 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>
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-<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>
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