Added: websites/staging/singa/trunk/content/v0.3.0/zh/distributed-training.html ============================================================================== --- websites/staging/singa/trunk/content/v0.3.0/zh/distributed-training.html (added) +++ websites/staging/singa/trunk/content/v0.3.0/zh/distributed-training.html Wed Apr 20 05:12:03 2016 @@ -0,0 +1,372 @@ +<!DOCTYPE html> +<!-- + | Generated by Apache Maven Doxia at 2016-04-20 + | Rendered using Apache Maven Fluido Skin 1.4 +--> +<html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en"> + <head> + <meta charset="UTF-8" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0" /> + <meta name="Date-Revision-yyyymmdd" content="20160420" /> + <meta http-equiv="Content-Language" content="en" /> + <title>Apache SINGA – Distributed Training</title> + <link rel="stylesheet" href="../../css/apache-maven-fluido-1.4.min.css" /> + <link rel="stylesheet" href="../../css/site.css" /> + <link rel="stylesheet" href="../../css/print.css" media="print" /> + + + + + + <script type="text/javascript" src="../../js/apache-maven-fluido-1.4.min.js"></script> + + + +<script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; 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class="externalLink" title="Apache Software Foundation"> + <span class="none"></span> + Apache Software Foundation</a> + </li> + + <li> + + <a href="http://www.comp.nus.edu.sg/~dbsystem/singa/"; class="externalLink" title="NUS Site"> + <span class="none"></span> + NUS Site</a> + </li> + </ul> + + + + <hr /> + + <div id="poweredBy"> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <a href="http://incubator.apache.org"; title="apache-incubator" class="builtBy"> + <img class="builtBy" alt="Apache Incubator" src="http://incubator.apache.org/images/egg-logo.png"; /> + </a> + </div> + </div> + </div> + + + <div id="bodyColumn" class="span10" > + + <h1>Distributed Training</h1> +<hr /> +<p>SINGA is designed for distributed training of large deep learning models with huge amount of training data. We also provide high-level descriptions of design behind SINGA’s distributed architecture. </p> + +<ul> + +<li> +<p><a href="architecture.html">System Architecture</a></p></li> + +<li> +<p><a href="frameworks.html">Training Frameworks</a></p></li> + +<li> +<p><a href="communication.html">System Communication</a></p></li> +</ul> +<p>SINGA supports different options for training a model in parallel, includeing data parallelism, model parallelism and hybrid parallelism.</p> + +<ul> + +<li><a href="hybrid.html">Hybrid Parallelism</a></li> +</ul> +<p>SINGA is intergrated with Mesos, so that distributed training can be started as a Mesos framework. Currently, the Mesos cluster can be set up from SINGA containers, i.e. we provide Docker images that bundles Mesos and SINGA together. Refer to the guide below for instructions as how to start and use the cluster.</p> + +<ul> + +<li><a href="mesos.html">Distributed training on Mesos</a></li> +</ul> +<p>SINGA can run on top of distributed storage system to achieve scalability. The current version of SINGA supports HDFS.</p> + +<ul> + +<li><a href="hdfs.html">Running SINGA on HDFS</a></li> +</ul> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p> + </div> + + + </div> + </footer> + </body> +</html>
Added: websites/staging/singa/trunk/content/v0.3.0/zh/index.html ============================================================================== --- websites/staging/singa/trunk/content/v0.3.0/zh/index.html (added) +++ websites/staging/singa/trunk/content/v0.3.0/zh/index.html Wed Apr 20 05:12:03 2016 @@ -0,0 +1,350 @@ +<!DOCTYPE html> +<!-- + | Generated by Apache Maven Doxia at 2016-04-20 + | Rendered using Apache Maven Fluido Skin 1.4 +--> +<html xmlns="http://www.w3.org/1999/xhtml"; xml:lang="en" lang="en"> + <head> + <meta charset="UTF-8" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0" /> + <meta name="Date-Revision-yyyymmdd" content="20160420" /> + <meta http-equiv="Content-Language" content="en" /> + <title>Apache SINGA – </title> + <link rel="stylesheet" href="../../css/apache-maven-fluido-1.4.min.css" /> + <link rel="stylesheet" href="../../css/site.css" /> + <link rel="stylesheet" href="../../css/print.css" media="print" /> + + + + + + <script type="text/javascript" src="../../js/apache-maven-fluido-1.4.min.js"></script> + + + +<script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; 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class="externalLink" title="Apache Software Foundation"> + <span class="none"></span> + Apache Software Foundation</a> + </li> + + <li> + + <a href="http://www.comp.nus.edu.sg/~dbsystem/singa/"; class="externalLink" title="NUS Site"> + <span class="none"></span> + NUS Site</a> + </li> + </ul> + + + + <hr /> + + <div id="poweredBy"> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <a href="http://incubator.apache.org"; title="apache-incubator" class="builtBy"> + <img class="builtBy" alt="Apache Incubator" src="http://incubator.apache.org/images/egg-logo.png"; /> + </a> + </div> + </div> + </div> + + + <div id="bodyColumn" class="span10" > + + <p>SINGA 中文文档</p> +<hr /> + +<ul> + +<li><a href="overview.html">简介</a></li> + +<li><a href="installation_source.html">安装</a></li> + +<li><a href="programming-guide.html">使用指南</a></li> +</ul> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. 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class="externalLink" title="Apache Software Foundation"> + <span class="none"></span> + Apache Software Foundation</a> + </li> + + <li> + + <a href="http://www.comp.nus.edu.sg/~dbsystem/singa/"; class="externalLink" title="NUS Site"> + <span class="none"></span> + NUS Site</a> + </li> + </ul> + + + + <hr /> + + <div id="poweredBy"> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <a href="http://incubator.apache.org"; title="apache-incubator" class="builtBy"> + <img class="builtBy" alt="Apache Incubator" src="http://incubator.apache.org/images/egg-logo.png"; /> + </a> + </div> + </div> + </div> + + + <div id="bodyColumn" class="span10" > + + <h1>从源程序安装SIGNA</h1> +<hr /> +<div class="section"> +<h2><a name="a"></a>依赖</h2> +<p>SINGA 在Linux平台上开发与测试。安装SINGA需要下拉列依赖库：</p> + +<ul> + +<li> +<p>glog version 0.3.3</p></li> + +<li> +<p>google-protobuf version 2.6.0</p></li> + +<li> +<p>openblas version >= 0.2.10</p></li> + +<li> +<p>zeromq version >= 3.2</p></li> + +<li> +<p>czmq version >= 3</p></li> + +<li> +<p>zookeeper version 3.4.6</p></li> +</ul> +<p>可选依赖包括：</p> + +<ul> + +<li>lmdb version 0.9.10</li> +</ul> +<p>你可以使用下列命令将所有的依赖库安装到$PREFIX文件夹下：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># make sure you are in the thirdparty folder +cd thirdparty +./install.sh all $PREFIX +</pre></div></div> +<p>如果$PREFIX不是一个系统路径（如：/esr/local/），请在继续安装前使用下述命令导出相关变量：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">export LD_LIBRARY_PATH=$PREFIX/lib:$LD_LIBRARY_PATH +export CPLUS_INCLUDE_PATH=$PREFIX/include:$CPLUS_INCLUDE_PATH +export LIBRARY_PATH=$PREFIX/lib:$LIBRARY_PATH +export PATH=$PREFIX/bin:$PATH +</pre></div></div> +<p>关于使用这个脚本的细节后文会详细介绍。</p></div> +<div class="section"> +<h2><a name="aSINGA"></a>从源程序安装SINGA</h2> +<p>SINGA 使用 GNU autotools 构建，需要GCC (version >= 4.8)。 有两种方式安装SINGA。</p> + +<ul> + +<li> +<p>如果你想使用最近的代码，请执行以下命令从 <a class="externalLink" href="https://github.com/apache/incubator-singa.git";>Github</a> 上克隆：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ git clone [email protected]:apache/incubator-singa.git +$ cd incubator-singa +$ ./autogen.sh +$ ./configure +$ make +</pre></div></div></li> +</ul> +<p>注意: 由于我们的疏忽，在加入Apache Incubator项目后，<a class="externalLink" href="https://github.com/orgs/nusinga";>nusinga</a> 帐号下的SINGA库（repo）并没有删除，但它早已没有更新，很抱歉给大家带来的不便。</p> + +<ul> + +<li> +<p>如果你下载了发布包，请按以下命令安装：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ tar xvf singa-xxx +$ cd singa-xxx +$ ./configure +$ make +</pre></div></div> +<p>SINGA的部分特性依赖于外部库，这些特性可以使用<tt>--enable-<feature></tt>编译。 比如，按准跟支持lmdb的SINGA，可以运行下面的命令：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./configure --enable-lmdb +</pre></div></div></li> +</ul> +<!-- - +Zhongle: please update the code to use the follow command + + $ make test + +After compilation, you will find the binary file singatest. Just run it! +More details about configure script can be found by running: + + $ ./configure -h --> +<p>SINGA编译成功后， <i>libsinga.so</i> 和可执行文件 <i>singa</i> 会生成在 <i>.libs/</i> 文件夹下。</p> +<p>如果缺失（或没有检测到）某些依赖库，可使用下面的脚本下载和安装：</p> +<!-- - +to be updated after zhongle changes the code to use + + ./install.sh libname \-\-prefix= --> + +<div class="source"> +<div class="source"><pre class="prettyprint"># must goto thirdparty folder +$ cd thirdparty +$ ./install.sh LIB_NAME PREFIX +</pre></div></div> +<p>如果没有指定安装路径，这些库会被安装在这些软件默认的安装路径下。比如，如果想在默认系统文件夹下安装<tt>zeromq</tt>，请执行以下命令：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./install.sh zeromq +</pre></div></div> +<p>或者，如果想安装到其他目录：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./install.sh zeromq PREFIX +</pre></div></div> +<p>也可以将所有的依赖库安装到 <i>/usr/local</i> 目录:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./install.sh all /usr/local +</pre></div></div> +<p>下表展示了各依赖库的第一个参数：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">LIB_NAME LIBRARIE +czmq* czmq lib +glog glog lib +lmdb lmdb lib +OpenBLAS OpenBLAS lib +protobuf Google protobuf +zeromq zeromq lib +zookeeper Apache zookeeper +</pre></div></div> +<p>*: 因为 <tt>czmq</tt> 依赖于 <tt>zeromq</tt>，下述脚本多提供一个参数，说明 <tt>zeromq</tt> 的位置。 <tt>czmq</tt> 的安装命令是：</p> +<!-- - +to be updated to + + $./install.sh czmq \-\-prefix=/usr/local \-\-zeromq=/usr/local/zeromq --> + +<div class="source"> +<div class="source"><pre class="prettyprint">$./install.sh czmq /usr/local -f=/usr/local/zeromq +</pre></div></div> +<p>执行后，<tt>czmq</tt> 会被安装在 <i>/usr/local</i>，上述最后一个路径指明了 zeromq 的路径。</p> +<div class="section"> +<h3><a name="a"></a>常见问题</h3> + +<ul> + +<li>Q1: 即使安装了 OpenBLAS，仍遇见 <tt>./configure --> cannot find blas_segmm() function</tt> 错误。</li> +</ul> +<p>A1: 该错误是指编译器找不着<tt>OpenBLAS</tt>，如果你安装在 $PREFIX (如, /opt/OpenBLAS)，你需要将路径导出，如下所示</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ export LIBRARY_PATH=$PREFIX/lib:$LIBRARY_PATH + # e.g., + $ export LIBRARY_PATH=/opt/OpenBLAS/lib:$LIBRARY_PATH +</pre></div></div> + +<ul> + +<li>Q2: 碰见错误<tt>cblas.h no such file or directory exists</tt>。</li> +</ul> +<p>Q2: 你需要将 cblas.h 所在文件夹包含到 CPLUS_INCLUDE_PATH 中，如：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ export CPLUS_INCLUDE_PATH=$PREFIX/include:$CPLUS_INCLUDE_PATH + # e.g., + $ export CPLUS_INCLUDE_PATH=/opt/OpenBLAS/include:$CPLUS_INCLUDE_PATH + # then reconfigure and make SINGA + $ ./configure + $ make +</pre></div></div> + +<ul> + +<li>Q3: 编译SINGA时，碰见错误<tt>SSE2 instruction set not enabled</tt>。</li> +</ul> +<p>A3: 你可以尝试以下命令:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ make CFLAGS='-msse2' CXXFLAGS='-msse2' +</pre></div></div> + +<ul> + +<li>Q4: 当我试着import .py文件时，从google.protobuf.internal 得到错误<tt>ImportError: cannot import name enum_type_wrapper</tt>。</li> +</ul> +<p>A4: 通过 <tt>make install</tt> 安装google protobuf后, 我们应该安装python运行时库。在protobuf源文件夹下运行：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ cd /PROTOBUF/SOURCE/FOLDER + $ cd python + $ python setup.py build + $ python setup.py install +</pre></div></div> +<p>如果你要在系统文件夹中安装python的运行时库，可能要用<tt>sudo</tt>。</p> + +<ul> + +<li>Q5: 遇见由gflags导致的链接错误。</li> +</ul> +<p>A5: SINGA不依赖gflags，但你可能在安装glog时安装了gflags。这种情况下你需要用 <i>thirdparty/install.sh</i> 重新将glog安装到另一文件夹，并将该文件夹路径导出到LDFLAGS 和 CPPFLAGS 中。</p> + +<ul> + +<li>Q6: 在mac OS X上编译SINGA和安装 <tt>glog</tt> 时，遇到了致命错误 <tt>'ext/slist' file not found</tt></li> +</ul> +<p>A6: 请单独安装<tt>glog</tt>，再尝试以下命令:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ make CFLAGS='-stdlib=libstdc++' CXXFLAGS='stdlib=libstdc++' +</pre></div></div> + +<ul> + +<li>Q7: 当我启动一个训练作业时，程序报错为 “ZOO_ERROR…zk retcode=-4…”。</li> +</ul> +<p>A7: 这是因为 zookeeper 没有启动，请启动 zookeeper 服务。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ ./bin/zk-service start +</pre></div></div> +<p>如果仍有这个错误，可能是没有java，你可以用下述命令查看</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ java --version +</pre></div></div> + +<ul> + +<li>Q8: 当我从源文件安装 OpenBLAS 时，被告知需要一个 fortran 编译器。</li> +</ul> +<p>A8: 按如下命令编译 OpenBLAS：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ make ONLY_CBLAS=1 +</pre></div></div> +<p>或者用apt-get安装</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ sudo apt-get install openblas-dev +</pre></div></div> +<p>或者</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ sudo yum install openblas-devel +</pre></div></div> +<p>后两个命令需要 root 权限，注意OpenBLAS安装后设置环境变量包含头文件和库的路径（参照 依赖 小节）</p> + +<ul> + +<li>Q9: 当我安装 protocol buffer 时，被告知 GLIBC++_3.4.20 not found in /usr/lib64/libstdc++.so.6.</li> +</ul> +<p>A9: 这说明链接器找到了 libstdc++.so.6，但是这个文件比用于编译和链接程序的GCC版本老。程序要求属于新版本GCC的libstdc++，所以必须告诉链接器怎么找到新版本的额libstdc++共享库。最简单的解决方法是找到正确的 libstdc++，并把它导出到 LD_LIBRARY_PATH 中。如, 如果GLIBC++_3.4.20 被下面的命令列出，</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ strings /usr/local/lib64/libstdc++.so.6|grep GLIBC++ +</pre></div></div> +<p>你只需这样设置你的环境变量：</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> $ export LD_LIBRARY_PATH=/usr/local/lib64:$LD_LIBRARY_PATH +</pre></div></div> + +<ul> + +<li>Q10: 当我在编译glog时，提示如下错误“src/logging_unittest.cc:83:20: error: ‘gflags’ is not a namespace-name”</li> +</ul> +<p>A10: 可能是你已经安装的gflags版本，其命名空间不是gflags，而是其他的，比如是’google’。因此glog不能找到 ‘gflags’ 命名空间。</p> +<p>编译glog不需要gflags，你可以修改 configure.ac 文件，忽略 gflags。</p> + +<ol style="list-style-type: decimal"> + +<li>cd to glog src directory</li> + +<li>修改 configure.ac 第125行，改为 “AC_CHECK_LIB(gflags, main, ac_cv_have_libgflags=0, ac_cv_have_libgflags=0)”</li> + +<li>autoreconf</li> +</ol> +<p>然后，请重新编译glog。</p></div></div> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. 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A MLP typically consists of multiple directly connected layers, with each layer fully connected to the next one. In this example, we will use SINGA to train a <a class="externalLink" href="http://arxiv.org/abs/1003.0358";>simple MLP model proposed by Ciresan</a> for classifying handwritten digits from the <a class="externalLink" href="http://yann.lecun.com/exdb/mnist/";>MNIST dataset</a>.</p> +<div class="section"> +<h2><a name="Running_instructions"></a>Running instructions</h2> +<p>Please refer to the <a href="installation.html">installation</a> page for instructions on building SINGA, and the <a href="quick-start.html">quick start</a> for instructions on starting zookeeper.</p> +<p>We have provided scripts for preparing the training and test dataset in <i>examples/cifar10/</i>.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in examples/mnist +$ cp Makefile.example Makefile +$ make download +$ make create +</pre></div></div> +<div class="section"> +<h3><a name="Training_on_CPU"></a>Training on CPU</h3> +<p>After the datasets are prepared, we start the training by</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/mnist/job.conf +</pre></div></div> +<p>After it is started, you should see output like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">Record job information to /tmp/singa-log/job-info/job-1-20150817-055231 +Executing : ./singa -conf /xxx/incubator-singa/examples/mnist/job.conf -singa_conf /xxx/incubator-singa/conf/singa.conf -singa_job 1 +E0817 07:15:09.211885 34073 cluster.cc:51] proc #0 -> 192.168.5.128:49152 (pid = 34073) +E0817 07:15:14.972231 34114 server.cc:36] Server (group = 0, id = 0) start +E0817 07:15:14.972520 34115 worker.cc:134] Worker (group = 0, id = 0) start +E0817 07:15:24.462602 34073 trainer.cc:373] Test step-0, loss : 2.341021, accuracy : 0.109100 +E0817 07:15:47.341076 34073 trainer.cc:373] Train step-0, loss : 2.357269, accuracy : 0.099000 +E0817 07:16:07.173364 34073 trainer.cc:373] Train step-10, loss : 2.222740, accuracy : 0.201800 +E0817 07:16:26.714855 34073 trainer.cc:373] Train step-20, loss : 2.091030, accuracy : 0.327200 +E0817 07:16:46.590946 34073 trainer.cc:373] Train step-30, loss : 1.969412, accuracy : 0.442100 +E0817 07:17:06.207080 34073 trainer.cc:373] Train step-40, loss : 1.865466, accuracy : 0.514800 +E0817 07:17:25.890033 34073 trainer.cc:373] Train step-50, loss : 1.773849, accuracy : 0.569100 +E0817 07:17:51.208935 34073 trainer.cc:373] Test step-60, loss : 1.613709, accuracy : 0.662100 +E0817 07:17:53.176766 34073 trainer.cc:373] Train step-60, loss : 1.659150, accuracy : 0.652600 +E0817 07:18:12.783370 34073 trainer.cc:373] Train step-70, loss : 1.574024, accuracy : 0.666000 +E0817 07:18:32.904942 34073 trainer.cc:373] Train step-80, loss : 1.529380, accuracy : 0.670500 +E0817 07:18:52.608111 34073 trainer.cc:373] Train step-90, loss : 1.443911, accuracy : 0.703500 +E0817 07:19:12.168465 34073 trainer.cc:373] Train step-100, loss : 1.387759, accuracy : 0.721000 +E0817 07:19:31.855865 34073 trainer.cc:373] Train step-110, loss : 1.335246, accuracy : 0.736500 +E0817 07:19:57.327133 34073 trainer.cc:373] Test step-120, loss : 1.216652, accuracy : 0.769900 +</pre></div></div> +<p>After the training of some steps (depends on the setting) or the job is finished, SINGA will <a href="checkpoint.html">checkpoint</a> the model parameters.</p></div> +<div class="section"> +<h3><a name="Training_on_GPU"></a>Training on GPU</h3> +<p>To train this example model on GPU, just add a field in the configuration file for the GPU device,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +gpu: 0 +</pre></div></div></div> +<div class="section"> +<h3><a name="Training_using_Python_script"></a>Training using Python script</h3> +<p>The python helpers come with SINGA 0.2 make it easy to configure the job. For example the job.conf is replaced with a simple python script mnist_mlp.py which has about 30 lines of code following the <a class="externalLink" href="http://keras.io/";>Keras API</a>.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -exec tool/python/examples/mnist_mlp.py +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Details"></a>Details</h2> +<p>To train a model in SINGA, you need to prepare the datasets, and a job configuration which specifies the neural net structure, training algorithm (BP or CD), SGD update algorithm (e.g. Adagrad), number of training/test steps, etc.</p> +<div class="section"> +<h3><a name="Data_preparation"></a>Data preparation</h3> +<p>Before using SINGA, you need to write a program to pre-process the dataset you use to a format that SINGA can read. Please refer to the <a href="data.html">Data Preparation</a> to get details about preparing this MNIST dataset.</p></div> +<div class="section"> +<h3><a name="Neural_net"></a>Neural net</h3> + +<div style="text-align: center"> +<img src="../images/example-mlp.png" style="width: 230px" alt="" /> +<br /><b>Figure 1 - Net structure of the MLP example. </b></img> +</div> +<p>Figure 1 shows the structure of the simple MLP model, which is constructed following <a class="externalLink" href="http://arxiv.org/abs/1003.0358";>Ciresan’s paper</a>. The dashed circle contains two layers which represent one feature transformation stage. There are 6 such stages in total. They sizes of the <a href="layer.html#innerproductlayer">InnerProductLayer</a>s in these circles decrease from 2500->2000->1500->1000->500->10.</p> +<p>Next we follow the guide in <a href="neural-net.html">neural net page</a> and <a href="layer.html">layer page</a> to write the neural net configuration.</p> + +<ul> + +<li> +<p>We configure an input layer to read the training/testing records from a disk file.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer { + name: "data" + type: kRecordInput + store_conf { + backend: "kvfile" + path: "examples/mnist/train_data.bin" + random_skip: 5000 + batchsize: 64 + shape: 784 + std_value: 127.5 + mean_value: 127.5 + } + exclude: kTest + } + +layer { + name: "data" + type: kRecordInput + store_conf { + backend: "kvfile" + path: "examples/mnist/test_data.bin" + batchsize: 100 + shape: 784 + std_value: 127.5 + mean_value: 127.5 + } + exclude: kTrain + } +</pre></div></div></li> +</ul> + +<ul> + +<li> +<p>All <a href="layer.html#innerproductlayer">InnerProductLayer</a>s are configured similarly as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "fc1" + type: kInnerProduct + srclayers:"data" + innerproduct_conf{ + num_output: 2500 + } + param{ + name: "w1" + ... + } + param{ + name: "b1" + .. + } +} +</pre></div></div> +<p>with the <tt>num_output</tt> decreasing from 2500 to 10.</p></li> + +<li> +<p>A <a href="layer.html#stanhlayer">STanhLayer</a> is connected to every InnerProductLayer except the last one. It transforms the feature via scaled tanh function.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "tanh1" + type: kSTanh + srclayers:"fc1" +} +</pre></div></div></li> + +<li> +<p>The final <a href="layer.html#softmaxloss">Softmax loss layer</a> connects to LabelLayer and the last STanhLayer.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "loss" + type:kSoftmaxLoss + softmaxloss_conf{ topk:1 } + srclayers:"fc6" + srclayers:"data" +} +</pre></div></div></li> +</ul></div> +<div class="section"> +<h3><a name="Updater"></a>Updater</h3> +<p>The <a href="updater.html#updater">normal SGD updater</a> is selected. The learning rate shrinks by 0.997 every 60 steps (i.e., one epoch).</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">updater{ + type: kSGD + learning_rate{ + base_lr: 0.001 + type : kStep + step_conf{ + change_freq: 60 + gamma: 0.997 + } + } +} +</pre></div></div></div> +<div class="section"> +<h3><a name="TrainOneBatch_algorithm"></a>TrainOneBatch algorithm</h3> +<p>The MLP model is a feed-forward model, hence <a href="train-one-batch#back-propagation">Back-propagation algorithm</a> is selected.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">train_one_batch { + alg: kBP +} +</pre></div></div></div> +<div class="section"> +<h3><a name="Cluster_setting"></a>Cluster setting</h3> +<p>The following configuration set a single worker and server for training. <a href="frameworks.html">Training frameworks</a> page introduces configurations of a couple of distributed training frameworks.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cluster { + nworker_groups: 1 + nserver_groups: 1 +} +</pre></div></div></div></div> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. 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