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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>Updater</h1> +<hr /> +<p>Every server in SINGA has an <a href="../api/classsinga_1_1Updater.html">Updater</a> instance that updates parameters based on gradients. In this page, the <i>Basic user guide</i> describes the configuration of an updater. The <i>Advanced user guide</i> present details on how to implement a new updater and a new learning rate changing method.</p> +<div class="section"> +<h2><a name="Basic_user_guide"></a>Basic user guide</h2> +<p>There are many different parameter updating protocols (i.e., subclasses of <tt>Updater</tt>). They share some configuration fields like</p> + +<ul> + +<li><tt>type</tt>, an integer for identifying an updater;</li> + +<li><tt>learning_rate</tt>, configuration for the <a href="../api/classsinga_1_1LRGenerator.html">LRGenerator</a> which controls the learning rate.</li> + +<li><tt>weight_decay</tt>, the co-efficient for <a class="externalLink" href="http://deeplearning.net/tutorial/gettingstarted.html#regularization";>L2 * regularization</a>.</li> + +<li><a class="externalLink" href="http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/";>momentum</a>.</li> +</ul> +<p>If you are not familiar with the above terms, you can get their meanings in <a class="externalLink" href="http://cs231n.github.io/neural-networks-3/#update";>this page provided by Karpathy</a>.</p> +<div class="section"> +<h3><a name="Configuration_of_built-in_updater_classes"></a>Configuration of built-in updater classes</h3> +<div class="section"> +<h4><a name="Updater"></a>Updater</h4> +<p>The base <tt>Updater</tt> implements the <a class="externalLink" href="http://cs231n.github.io/neural-networks-3/#sgd";>vanilla SGD algorithm</a>. Its configuration type is <tt>kSGD</tt>. Users need to configure at least the <tt>learning_rate</tt> field. <tt>momentum</tt> and <tt>weight_decay</tt> are optional fields.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">updater{ + type: kSGD + momentum: float + weight_decay: float + learning_rate { + ... + } +} +</pre></div></div></div> +<div class="section"> +<h4><a name="AdaGradUpdater"></a>AdaGradUpdater</h4> +<p>It inherits the base <tt>Updater</tt> to implement the <a class="externalLink" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf";>AdaGrad</a> algorithm. Its type is <tt>kAdaGrad</tt>. <tt>AdaGradUpdater</tt> is configured similar to <tt>Updater</tt> except that <tt>momentum</tt> is not used.</p></div> +<div class="section"> +<h4><a name="NesterovUpdater"></a>NesterovUpdater</h4> +<p>It inherits the base <tt>Updater</tt> to implements the <a class="externalLink" href="http://arxiv.org/pdf/1212.0901v2.pdf";>Nesterov</a> (section 3.5) updating protocol. Its type is <tt>kNesterov</tt>. <tt>learning_rate</tt> and <tt>momentum</tt> must be configured. <tt>weight_decay</tt> is an optional configuration field.</p></div> +<div class="section"> +<h4><a name="RMSPropUpdater"></a>RMSPropUpdater</h4> +<p>It inherits the base <tt>Updater</tt> to implements the <a class="externalLink" href="http://cs231n.github.io/neural-networks-3/#sgd";>RMSProp algorithm</a> proposed by <a class="externalLink" href="http://www.cs.toronto.edu/%7Etijmen/csc321/slides/lecture_slides_lec6.pdf";>Hinton</a>(slide 29). Its type is <tt>kRMSProp</tt>.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">updater { + type: kRMSProp + rmsprop_conf { + rho: float # [0,1] + } +} +</pre></div></div></div></div> +<div class="section"> +<h3><a name="Configuration_of_learning_rate"></a>Configuration of learning rate</h3> +<p>The <tt>learning_rate</tt> field is configured as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + type: ChangeMethod + base_lr: float # base/initial learning rate + ... # fields to a specific changing method +} +</pre></div></div> +<p>The common fields include <tt>type</tt> and <tt>base_lr</tt>. SINGA provides the following <tt>ChangeMethod</tt>s.</p> +<div class="section"> +<h4><a name="kFixed"></a>kFixed</h4> +<p>The <tt>base_lr</tt> is used for all steps.</p></div> +<div class="section"> +<h4><a name="kLinear"></a>kLinear</h4> +<p>The updater should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + base_lr: float + linear_conf { + freq: int + final_lr: float + } +} +</pre></div></div> +<p>Linear interpolation is used to change the learning rate,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">lr = (1 - step / freq) * base_lr + (step / freq) * final_lr +</pre></div></div></div> +<div class="section"> +<h4><a name="kExponential"></a>kExponential</h4> +<p>The udapter should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + base_lr: float + exponential_conf { + freq: int + } +} +</pre></div></div> +<p>The learning rate for <tt>step</tt> is</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">lr = base_lr / 2^(step / freq) +</pre></div></div></div> +<div class="section"> +<h4><a name="kInverseT"></a>kInverseT</h4> +<p>The updater should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + base_lr: float + inverset_conf { + final_lr: float + } +} +</pre></div></div> +<p>The learning rate for <tt>step</tt> is</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">lr = base_lr / (1 + step / final_lr) +</pre></div></div></div> +<div class="section"> +<h4><a name="kInverse"></a>kInverse</h4> +<p>The updater should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + base_lr: float + inverse_conf { + gamma: float + pow: float + } +} +</pre></div></div> +<p>The learning rate for <tt>step</tt> is</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">lr = base_lr * (1 + gamma * setp)^(-pow) +</pre></div></div></div> +<div class="section"> +<h4><a name="kStep"></a>kStep</h4> +<p>The updater should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + base_lr : float + step_conf { + change_freq: int + gamma: float + } +} +</pre></div></div> +<p>The learning rate for <tt>step</tt> is</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">lr = base_lr * gamma^ (step / change_freq) +</pre></div></div></div> +<div class="section"> +<h4><a name="kFixedStep"></a>kFixedStep</h4> +<p>The updater should be configured like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + fixedstep_conf { + step: int + step_lr: float + + step: int + step_lr: float + + ... + } +} +</pre></div></div> +<p>Denote the i-th tuple as (step[i], step_lr[i]), then the learning rate for <tt>step</tt> is,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">step_lr[k] +</pre></div></div> +<p>where step[k] is the smallest number that is larger than <tt>step</tt>.</p></div></div></div> +<div class="section"> +<h2><a name="Advanced_user_guide"></a>Advanced user guide</h2> +<div class="section"> +<h3><a name="Implementing_a_new_Updater_subclass"></a>Implementing a new Updater subclass</h3> +<p>The base Updater class has one virtual function,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class Updater{ + public: + virtual void Update(int step, Param* param, float grad_scale = 1.0f) = 0; + + protected: + UpdaterProto proto_; + LRGenerator lr_gen_; +}; +</pre></div></div> +<p>It updates the values of the <tt>param</tt> based on its gradients. The <tt>step</tt> argument is for deciding the learning rate which may change through time (step). <tt>grad_scale</tt> scales the original gradient values. This function is called by servers once it receives all gradients for the same <tt>Param</tt> object.</p> +<p>To implement a new Updater subclass, users must override the <tt>Update</tt> function.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class FooUpdater : public Updater { + void Update(int step, Param* param, float grad_scale = 1.0f) override; +}; +</pre></div></div> +<p>Configuration of this new updater can be declared similar to that of a new layer,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in user.proto +FooUpdaterProto { + optional int32 c = 1; +} + +extend UpdaterProto { + optional FooUpdaterProto fooupdater_conf= 101; +} +</pre></div></div> +<p>The new updater should be registered in the <a href="programming-guide.html">main function</a></p> + +<div class="source"> +<div class="source"><pre class="prettyprint">driver.RegisterUpdater<FooUpdater>("FooUpdater"); +</pre></div></div> +<p>Users can then configure the job as</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in job.conf +updater { + user_type: "FooUpdater" # must use user_type with the same string identifier as the one used for registration + fooupdater_conf { + c : 20; + } +} +</pre></div></div></div> +<div class="section"> +<h3><a name="Implementing_a_new_LRGenerator_subclass"></a>Implementing a new LRGenerator subclass</h3> +<p>The base <tt>LRGenerator</tt> is declared as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">virtual float Get(int step); +</pre></div></div> +<p>To implement a subclass, e.g., <tt>FooLRGen</tt>, users should declare it like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class FooLRGen : public LRGenerator { + public: + float Get(int step) override; +}; +</pre></div></div> +<p>Configuration of <tt>FooLRGen</tt> can be defined using a protocol message,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in user.proto +message FooLRProto { + ... +} + +extend LRGenProto { + optional FooLRProto foolr_conf = 101; +} +</pre></div></div> +<p>The configuration is then like,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">learning_rate { + user_type : "FooLR" # must use user_type with the same string identifier as the one used for registration + base_lr: float + foolr_conf { + ... + } +} +</pre></div></div> +<p>Users have to register this subclass in the main function,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> driver.RegisterLRGenerator<FooLRGen, std::string>("FooLR") +</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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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>SINGA 아키텍처</h1> +<hr /> +<div class="section"> +<h2><a name="a_"></a>논리적 아키텍처</h2> +<p><img src="../../images/logical.png" style="width:550px" alt="" /> +<p> <b> Fig.1 - 시스템 아키텍처 </b> </p> +<p>SINGA는 다양한 분산 <a href="frameworks.html">트레이닝 프레임워크</a> (동기 혹은 비동기 트레이닝)을 지원하는 구조를 가지고 있습니다. 특징으로는 여러 server그룹 과 worker그룹을 가지고 있습니다.</p> + +<ul> + +<li> +<p><b>Server 그룹</b></p> +<p>Server그룹은 모델 매개 변수의 복제를 가지고 worker그룹의 요청에 따라 매개 변수의 업데이트를 담당합니다. 인접된 server그룹들은 매개 변수를 정기적으로 동기화합니다. 일반적으로 하나의 server그룹은 여러 server로 구성되며, 각 server는 모델 매개 변수의 분할 된 부분을 담당합니다.</p></li> + +<li> +<p><b>Worker 그룹</b></p> +<p>각 worker그룹은 하나의 server그룹과 통신합니다. 하나의 worker그룹은 매개 변수의 구배계산을 담당합니다. 또한 분할 된 일부 데이터를 써서 “전체”모델의 레프리카를 트레이닝합니다. 모든 worker그룹들은 해당 server그룹들과 비동기 통신을 합니다. 그러나 같은 worker그룹의 worker들은 동기합니다.</p></li> +</ul> +<p>동일 그룹 내에서 worker들의 분산 트레이닝에는 많은 방법이 있습니다.</p> + +<ul> + +<li> +<p><b>모델 병렬화</b></p> +<p>각 worker그룹에 배정 된 모든 데이터에 대해 매개 변수의 부분 집합을 계산합니다.</p></li> + +<li> +<p><b>데이터 병렬화</b></p> +<p>각 worker는 배분 된 데이터의 부분 집합에 대해 모든 매개 변수를 계산합니다.</p></li> + +<li> +<p><b>하이브리드 병렬화</b></p> +<p>위의 방법을 조합한 하이브리드 병렬화를 지원합니다.</p></li> +</ul></div> +<div class="section"> +<h2><a name="a"></a>임플리멘테이션</h2> +<p>SINGA에서 servers 와 workers는 다른 스레드에서 실행되는 유닛입니다.</p> +<p>이들은 <a href="communication.html">messages</a>를 이용하여 통신을 합니다. 모든 프로세스는 메인 스레드인 stub을 실행하여 로컬 messages를 수집하고 대응되는 receiver에 전송합니다.</p> +<p>각 server그룹과 worker그룹은 “전체”모델 레프리카인 <i>ParamShard</i> 를 가집니다. 만약 workers 와 servers 가 동일한 프로세스에서 실행 된다면, 그 <i>ParamShard</i> (파티션)은 메모리 공간을 공유하도록 설정됩니다. 이 경우 다른 실행 유닛 사이를 오가는 messages는 통신 비용을 줄이기 위해 데이터의 포인터 만 포함합니다. 프로세스 간 통신의 ¬ bd;우와는 달리 messages는 파라미터 값을 포함합니다.</p></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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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>CheckPoint</h1> +<hr /> +<p>SINGA checkpoints model parameters onto disk periodically according to user configured frequency. By checkpointing model parameters, we can</p> + +<ol style="list-style-type: decimal"> + +<li> +<p>resume the training from the last checkpointing. For example, if the program crashes before finishing all training steps, we can continue the training using checkpoint files.</p></li> + +<li> +<p>use them to initialize a similar model. For example, the parameters from training a RBM model can be used to initialize a <a href="rbm.html">deep auto-encoder</a> model.</p></li> +</ol> +<div class="section"> +<h2><a name="Configuration"></a>Configuration</h2> +<p>Checkpointing is controlled by two configuration fields:</p> + +<ul> + +<li><tt>checkpoint_after</tt>, start checkpointing after this number of training steps,</li> + +<li><tt>checkpoint_freq</tt>, frequency of doing checkpointing.</li> +</ul> +<p>For example,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +checkpoint_after: 100 +checkpoint_frequency: 300 +... +</pre></div></div> +<p>Checkpointing files are located at <i>WORKSPACE/checkpoint/stepSTEP-workerWORKERID</i>. <i>WORKSPACE</i> is configured in</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cluster { + workspace: +} +</pre></div></div> +<p>For the above configuration, after training for 700 steps, there would be two checkpointing files,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">step400-worker0 +step700-worker0 +</pre></div></div></div> +<div class="section"> +<h2><a name="Application_-_resuming_training"></a>Application - resuming training</h2> +<p>We can resume the training from the last checkpoint (i.e., step 700) by,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf JOB_CONF -resume +</pre></div></div> +<p>There is no change to the job configuration.</p></div> +<div class="section"> +<h2><a name="Application_-_model_initialization"></a>Application - model initialization</h2> +<p>We can also use the checkpointing file from step 400 to initialize a new model by configuring the new job as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +checkpoint : "WORKSPACE/checkpoint/step400-worker0" +... +</pre></div></div> +<p>If there are multiple checkpointing files for the same snapshot due to model partitioning, all the checkpointing files should be added,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +checkpoint : "WORKSPACE/checkpoint/step400-worker0" +checkpoint : "WORKSPACE/checkpoint/step400-worker1" +... +</pre></div></div> +<p>The training command is the same as starting a new job,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf JOB_CONF +</pre></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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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>CNN Example</h1> +<hr /> +<p>Convolutional neural network (CNN) is a type of feed-forward artificial neural network widely used for image and video classification. In this example, we will use a deep CNN model to do image classification for the <a class="externalLink" href="http://www.cs.toronto.edu/~kriz/cifar.html";>CIFAR10 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/cifar10 +$ cp Makefile.example Makefile +$ make download +$ make create +</pre></div></div> +<p>We can start the training by</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div> +<p>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-2-20150817-055601 +Executing : ./singa -conf /xxx/incubator-singa/examples/cifar10/job.conf -singa_conf /xxx/incubator-singa/conf/singa.conf -singa_job 2 +E0817 06:56:18.868259 33849 cluster.cc:51] proc #0 -> 192.168.5.128:49152 (pid = 33849) +E0817 06:56:18.928452 33871 server.cc:36] Server (group = 0, id = 0) start +E0817 06:56:18.928469 33872 worker.cc:134] Worker (group = 0, id = 0) start +E0817 06:57:13.657302 33849 trainer.cc:373] Test step-0, loss : 2.302588, accuracy : 0.077900 +E0817 06:57:17.626708 33849 trainer.cc:373] Train step-0, loss : 2.302578, accuracy : 0.062500 +E0817 06:57:24.142645 33849 trainer.cc:373] Train step-30, loss : 2.302404, accuracy : 0.131250 +E0817 06:57:30.813354 33849 trainer.cc:373] Train step-60, loss : 2.302248, accuracy : 0.156250 +E0817 06:57:37.556655 33849 trainer.cc:373] Train step-90, loss : 2.301849, accuracy : 0.175000 +E0817 06:57:44.971276 33849 trainer.cc:373] Train step-120, loss : 2.301077, accuracy : 0.137500 +E0817 06:57:51.801949 33849 trainer.cc:373] Train step-150, loss : 2.300410, accuracy : 0.135417 +E0817 06:57:58.682281 33849 trainer.cc:373] Train step-180, loss : 2.300067, accuracy : 0.127083 +E0817 06:58:05.578366 33849 trainer.cc:373] Train step-210, loss : 2.300143, accuracy : 0.154167 +E0817 06:58:12.518497 33849 trainer.cc:373] Train step-240, loss : 2.295912, accuracy : 0.185417 +</pre></div></div> +<p>After training 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"> +<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 convert the dataset into a format that SINGA can read. Please refer to the <a href="data.html#example---cifar-dataset">Data Preparation</a> to get details about preparing this CIFAR10 dataset.</p></div> +<div class="section"> +<h3><a name="Neural_net"></a>Neural net</h3> +<p>Figure 1 shows the net structure of the CNN model we used in this example, which is set following <a class="externalLink" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg.";>Alex</a> The dashed circle represents one feature transformation stage, which generally has four layers as shown in the figure. Sometimes the rectifier layer and normalization layer are omitted or swapped in one stage. For this example, there are 3 such stages.</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> + +<div style="text-align: center"> +<img src="../images/example-cnn.png" style="width: 200px" alt="" /> <br /> +<b>Figure 1 - Net structure of the CNN example.</b></img> +</div> + +<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/cifar10/train_data.bin" + mean_file: "examples/cifar10/image_mean.bin" + batchsize: 64 + random_skip: 5000 + shape: 3 + shape: 32 + shape: 32 + } + exclude: kTest # exclude this layer for the testing net +} +layer{ + name: "data" + type: kRecordInput + store_conf { + backend: "kvfile" + path: "examples/cifar10/test_data.bin" + mean_file: "examples/cifar10/image_mean.bin" + batchsize: 100 + shape: 3 + shape: 32 + shape: 32 + } + exclude: kTrain # exclude this layer for the training net +} +</pre></div></div></li> +</ul> + +<ul> + +<li> +<p>We configure layers for the feature transformation as follows (all layers are built-in layers in SINGA; hyper-parameters of these layers are set according to <a class="externalLink" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg";>Alex’s setting</a>).</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer { + name: "conv1" + type: kConvolution + srclayers: "data" + convolution_conf {... } + ... +} +layer { + name: "pool1" + type: kPooling + srclayers: "conv1" + pooling_conf {... } +} +layer { + name: "relu1" + type: kReLU + srclayers:"pool1" +} +layer { + name: "norm1" + type: kLRN + lrn_conf {... } + srclayers:"relu1" +} +</pre></div></div></li> +</ul> +<p>The configurations for another 2 stages are omitted here.</p> + +<ul> + +<li> +<p>There is an <a href="layer.html#innerproductlayer">inner product layer</a> after the 3 transformation stages, which is configured with 10 output units, i.e., the number of total labels. The weight matrix Param is configured with a large weight decay scale to reduce the over-fitting.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer { + name: "ip1" + type: kInnerProduct + srclayers:"pool3" + innerproduct_conf { + num_output: 10 + } + param { + name: "w4" + wd_scale:250 + ... + } + param { + name: "b4" + ... + } +} +</pre></div></div></li> + +<li> +<p>The last layer is a <a href="layer.html#softmaxloss">Softmax loss layer</a></p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "loss" + type: kSoftmaxLoss + softmaxloss_conf{ topk:1 } + srclayers:"ip1" + 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 is changed like going down stairs, and is configured using the <a href="updater.html#kfixedstep">kFixedStep</a> type.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"> updater{ + type: kSGD + weight_decay:0.004 + learning_rate { + type: kFixedStep + fixedstep_conf:{ + step:0 # lr for step 0-60000 is 0.001 + step:60000 # lr for step 60000-65000 is 0.0001 + step:65000 # lr for step 650000- is 0.00001 + step_lr:0.001 + step_lr:0.0001 + step_lr:0.00001 + } + } + } +</pre></div></div></div> +<div class="section"> +<h3><a name="TrainOneBatch_algorithm"></a>TrainOneBatch algorithm</h3> +<p>The CNN model is a feed forward model, thus should be configured to use the <a href="train-one-batch.html#back-propagation">Back-propagation algorithm</a>.</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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