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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>Programming Guide</h1> +<hr /> +<p>To submit a training job, users must provide the configuration of the four components shown in Figure 1:</p> + +<ul> + +<li>a <a href="neural-net.html">NeuralNet</a> describing the neural net structure with the detailed layer setting and their connections;</li> + +<li>a <a href="train-one-batch.html">TrainOneBatch</a> algorithm which is tailored for different model categories;</li> + +<li>an <a href="updater.html">Updater</a> defining the protocol for updating parameters at the server side;</li> + +<li>a <a href="distributed-training.html">Cluster Topology</a> specifying the distributed architecture of workers and servers.</li> +</ul> +<p>The <i>Basic user guide</i> section describes how to submit a training job using built-in components; while the <i>Advanced user guide</i> section presents details on writing user’s own main function to register components implemented by themselves. In addition, the training data must be prepared, which has the same <a href="data.html">process</a> for both advanced users and basic users.</p> +<p><img src="../../images/overview.png" align="center" width="400px" alt="" /> <span><b>Figure 1 - SINGA overview.</b></span></p> +<div class="section"> +<h2><a name="Basic_user_guide"></a>Basic user guide</h2> +<p>Users can use the default main function provided SINGA to submit the training job. For this case, a job configuration file written as a google protocol buffer message for the <a href="../api/classsinga_1_1JobProto.html">JobProto</a> must be provided in the command line,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf <path to job conf> [-resume] +</pre></div></div> +<p><tt>-resume</tt> is for continuing the training from last <a href="checkpoint.html">checkpoint</a>. The <a href="mlp.html">MLP</a> and <a href="cnn.html">CNN</a> examples use built-in components. Please read the corresponding pages for their job configuration files. The subsequent pages will illustrate the details on each component of the configuration.</p></div> +<div class="section"> +<h2><a name="Advanced_user_guide"></a>Advanced user guide</h2> +<p>If a user’s model contains some user-defined components, e.g., <a href="updater.html">Updater</a>, he has to write a main function to register these components. It is similar to Hadoop’s main function. Generally, the main function should</p> + +<ul> + +<li> +<p>initialize SINGA, e.g., setup logging.</p></li> + +<li> +<p>register user-defined components.</p></li> + +<li> +<p>create and pass the job configuration to SINGA driver</p></li> +</ul> +<p>An example main function is like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">#include "singa.h" +#include "user.h" // header for user code + +int main(int argc, char** argv) { + singa::Driver driver; + driver.Init(argc, argv); + bool resume; + // parse resume option from argv. + + // register user defined layers + driver.RegisterLayer<FooLayer>(kFooLayer); + // register user defined updater + driver.RegisterUpdater<FooUpdater>(kFooUpdater); + ... + auto jobConf = driver.job_conf(); + // update jobConf + + driver.Train(resume, jobConf); + return 0; +} +</pre></div></div> +<p>The Driver class’ <tt>Init</tt> method will load a job configuration file provided by users as a command line argument (<tt>-conf <job conf></tt>). It contains at least the cluster topology and returns the <tt>jobConf</tt> for users to update or fill in configurations of neural net, updater, etc. If users define subclasses of Layer, Updater, Worker and Param, they should register them through the driver. Finally, the job configuration is submitted to the driver which starts the training.</p> +<p>We will provide helper functions to make the configuration easier in the future, like <a class="externalLink" href="https://github.com/fchollet/keras";>keras</a>.</p> +<p>Users need to compile and link their code (e.g., layer implementations and the main file) with SINGA library (<i>.libs/libsinga.so</i>) to generate an executable file, e.g., with name <i>mysinga</i>. To launch the program, users just pass the path of the <i>mysinga</i> and base job configuration to <i>./bin/singa-run.sh</i>.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf <path to job conf> -exec <path to mysinga> [other arguments] +</pre></div></div> +<p>The <a href="rnn.html">RNN application</a> provides a full example of implementing the main function for training a specific RNN model.</p></div> + </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>
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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>クイック スタート</h1> +<hr /> +<div class="section"> +<h2><a name="SINGA_"></a>SINGA セットアップ</h2> +<p>SINGAのインストールについては<a href="installation.html">こちら</a>をご覧ください。</p> +<div class="section"> +<h3><a name="Zookeeper_"></a>Zookeeper の実行</h3> +<p>SINGAのトレーニングは　<a class="externalLink" href="https://zookeeper.apache.org/";>zookeeper</a> を利用します。まずは zookeeper サービスが開始されていることを確認してください。</p> +<p>準備された thirdparty のスクリプトを使って zookeeper をインストールした場合、次のスクリプトを実行してください。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">#goto top level folder +cd SINGA_ROOT +./bin/zk-service.sh start +</pre></div></div> +<p>(<tt>./bin/zk-service.sh stop</tt> // zookeeper の停止).</p> +<p>デフォルトのポートを使用せずに zookeeper をスタートさせる時は、<tt>conf/singa.conf</tt>を編集してください。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">zookeeper_host: "localhost:YOUR_PORT" +</pre></div></div></div></div> +<div class="section"> +<h2><a name="a"></a>スタンドアローンモードでの実行</h2> +<p>スタンドアローンモードでSINGAを実行するとは、<a class="externalLink" href="http://mesos.apache.org/";>Mesos</a> や <a class="externalLink" href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html";>YARN</a> のようなクラスターマネージャー利用しない場合のことを言います。</p> +<div class="section"> +<h3><a name="Single_"></a>Single ノードでのトレーニング</h3> +<p>１つのプロセスがローンチされます。 例として、 <a class="externalLink" href="http://www.cs.toronto.edu/~kriz/cifar.html";>CIFAR-10</a> データセットを利用して <a class="externalLink" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks";>CNN モデル</a> をトレーニングさせます。 ハイパーパラメーターは、<a class="externalLink" href="https://code.google.com/p/cuda-convnet/";>cuda-convnet</a> に基づいて設定されてあります。 詳細は、<a href="cnn.html">CNN サン̍ 7;ル</a> のページをご覧ください。</p> +<div class="section"> +<h4><a name="a"></a>データと、ジョブ設定</h4> +<p>データセットのダウンロードと、Triaing や Test のためのデータシャードの生成は次のように行います。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cd examples/cifar10/ +cp Makefile.example Makefile +make download +make create +</pre></div></div> +<p>Training と Test データセットは、それぞれ <i>cifar10-train-shard</i> と <i>cifar10-test-shard</i> フォルダーに作られます。　すべての画像の特徴平均を記述した <i>image_mean.bin</i> ファイルも作成されます。</p> +<p>CNN モデルのトレーニングに必要なソースコードはすべてSINGAに組み込まれています。コードを追加する必要はありません。 ジョブ設定ファイル (<i>job.conf</i>) を指定して、スクリプト(<i>../../bin/singa-run.sh</i>) を実行します。 SINGAのコードを変更、または追加する時は、<a href="programming-guide.html">プログラミングガイド</a>をご覧く&# x3060;さい。</p></div> +<div class="section"> +<h4><a name="a"></a>並列化なしのトレーニング</h4> +<p>Cluster Topology のデフォルト値は、１つの worker と　１つの server となっています。 データとニューラルネットの並列化はされません。</p> +<p>トレーニングを開始するには次のスクリプトを実行します。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># goto top level folder +cd ../../ +./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div> +<p>現在、起動中のジョブのリストを表示するには</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-console.sh list + +JOB ID |NUM PROCS +----------|----------- +24 |1 +</pre></div></div> +<p>ジョブの強制終了をするには</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-console.sh kill JOB_ID +</pre></div></div> +<p>ログとジョブの情報は <i>/tmp/singa-log</i> フォルダーに保存されます。 <i>conf/singa.conf</i> ファイルの <tt>log-dir</tt>で変更可能です。</p></div> +<div class="section"> +<h4><a name="a"></a>非同期、並列トレーニング</h4> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +cluster { + nworker_groups: 2 + nworkers_per_procs: 2 + workspace: "examples/cifar10/" +} +</pre></div></div> +<p>複数の worker グループをローンチすることによって、 In SINGA, <a href="architecture.html">非同期トレーニング</a> を実行することが出来ます。 例えば、<i>job.conf</i> を上記のように変更します。 デフォルトでは、１つの worker グループが１つの worker を持つよう設定されています。 上記の設定では、１つのプロセスに２つの worker &#x 304c;設定されているので、２つの worker グループが同じプロセスとして実行されます。 結果、インメモリ <a href="frameworks.html">Downpour</a> トレーニングフレームワークとして、実行されます。</p> +<p>ユーザーは、データの分散を気にする必要はありません。 ランダムオフセットに従い、各 worker グループに、データが振り分けられます。 各 worker は異なるデータパーティションを担当します。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +neuralnet { + layer { + ... + sharddata_conf { + random_skip: 5000 + } + } + ... +} +</pre></div></div> +<p>スクリプト実行:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div> +<div class="section"> +<h4><a name="a"></a>同期、並列トレーニング</h4> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +cluster { + nworkers_per_group: 2 + nworkers_per_procs: 2 + workspace: "examples/cifar10/" +} +</pre></div></div> +<p>１つのworkerグループとして複数のworkerをローンチすることで <a href="architecture.html">同期トレーニング</a>を実行することが出来ます。 例えば、<i>job.conf</i> ファイルを上記のように変更します。 上記の設定では、１つの worker グループに２つの worker が設定されました。 worker 達はグループ内で同期します。 これは、イン&# x30e1;モリ <a href="frameworks.html">sandblaster</a> として実行されます。 モデルは２つのworkerに分割されます。各レイヤーが２つのworkerに振り分けられます。 振り分けられたレイヤーはオリジナルのレイヤーと機能は同じですが、特徴インスタンスの数が <tt>B/g</tt> になります。 ここで、<tt>B</tt>はミニバッチのインスタンスのe 70;で、<tt>g</tt>はグループ内の worker の数です。 <a href="neural-net.html">別のスキーム</a> を利用したレイヤー（ニューラルネットワーク）パーティション方法もあります。</p> +<p>他の設定はすべて「並列化なし」の場合と同じです。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div></div> +<div class="section"> +<h3><a name="a"></a>クラスタ上でのトレーニング</h3> +<p>クラスター設定を変更して、上記トレーニングフレームワークの拡張を行います。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">nworker_per_procs: 1 +</pre></div></div> +<p>すべてのプロセスは１つのworkerスレッドを生成します。 結果、worker 達は異なるプロセス（ノード）内で生成されます。 クラスター内のノードを特定するには、<i>SINGA_ROOT/conf/</i> の <i>hostfile</i> の設定が必要です。</p> +<p>e.g.,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">logbase-a01 +logbase-a02 +</pre></div></div> +<p>zookeeper location も設定する必要があります。</p> +<p>e.g.,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">#conf/singa.conf +zookeeper_host: "logbase-a01" +</pre></div></div> +<p>スクリプトの実行は「Single ノード トレーニング」と同じです。</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Mesos"></a>Mesos　での実行</h2> +<p><i>working</i>…</p></div> +<div class="section"> +<h2><a name="a"></a>次へ</h2> +<p>SINGAのコード変更や追加に関する詳細は、<a href="programming-guide.html">プログラミングガイド</a> をご覧ください。</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>RBM Example</h1> +<hr /> +<p>This example uses SINGA to train 4 RBM models and one auto-encoder model over the <a class="externalLink" href="http://yann.lecun.com/exdb/mnist/";>MNIST dataset</a>. The auto-encoder model is trained to reduce the dimensionality of the MNIST image feature. The RBM models are trained to initialize parameters of the auto-encoder model. This example application is from <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>.</p> +<div class="section"> +<h2><a name="Running_instructions"></a>Running instructions</h2> +<p>Running scripts are provided in <i>SINGA_ROOT/examples/rbm</i> folder.</p> +<p>The MNIST dataset has 70,000 handwritten digit images. The <a href="data.html">data preparation</a> page has details on converting this dataset into SINGA recognizable format. Users can simply run the following commands to download and convert the dataset.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># at SINGA_ROOT/examples/mnist/ +$ cp Makefile.example Makefile +$ make download +$ make create +</pre></div></div> +<p>The training is separated into two phases, namely pre-training and fine-tuning. The pre-training phase trains 4 RBMs in sequence,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># at SINGA_ROOT/ +$ ./bin/singa-run.sh -conf examples/rbm/rbm1.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm2.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm3.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm4.conf +</pre></div></div> +<p>The fine-tuning phase trains the auto-encoder by,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./bin/singa-run.sh -conf examples/rbm/autoencoder.conf +</pre></div></div></div> +<div class="section"> +<h2><a name="Training_details"></a>Training details</h2> +<div class="section"> +<h3><a name="RBM1"></a>RBM1</h3> +<p><img src="../images/example-rbm1.png" align="center" width="200px" alt="" /> <span><b>Figure 1 - RBM1.</b></span></p> +<p>The neural net structure for training RBM1 is shown in Figure 1. The data layer and parser layer provides features for training RBM1. The visible layer (connected with parser layer) of RBM1 accepts the image feature (784 dimension). The hidden layer is set to have 1000 neurons (units). These two layers are configured as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"mnist" + srclayers:"RBMHid" + rbm_conf{ + hdim: 1000 + } + param{ + name: "w1" + init{ + type: kGaussian + mean: 0.0 + std: 0.1 + } + } + param{ + name: "b11" + init{ + type: kConstant + value: 0.0 + } + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 1000 + } + param{ + name: "w1_" + share_from: "w1" + } + param{ + name: "b12" + init{ + type: kConstant + value: 0.0 + } + } +} +</pre></div></div> +<p>For RBM, the weight matrix is shared by the visible and hidden layers. For instance, <tt>w1</tt> is shared by <tt>vis</tt> and <tt>hid</tt> layers shown in Figure 1. In SINGA, we can configure the <tt>share_from</tt> field to enable <a href="param.html">parameter sharing</a> as shown above for the param <tt>w1</tt> and <tt>w1_</tt>.</p> +<p><a href="train-one-batch.html#contrastive-divergence">Contrastive Divergence</a> is configured as the algorithm for <a href="train-one-batch.html">TrainOneBatch</a>. Following Hinton’s paper, we configure the <a href="updater.html">updating protocol</a> as follows,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># Updater Configuration +updater{ + type: kSGD + momentum: 0.2 + weight_decay: 0.0002 + learning_rate{ + base_lr: 0.1 + type: kFixed + } +} +</pre></div></div> +<p>Since the parameters of RBM0 will be used to initialize the auto-encoder, we should configure the <tt>workspace</tt> field to specify a path for the checkpoint folder. For example, if we configure it as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cluster { + workspace: "examples/rbm/rbm1/" +} +</pre></div></div> +<p>Then SINGA will <a href="checkpoint.html">checkpoint the parameters</a> into <i>examples/rbm/rbm1/</i>.</p></div> +<div class="section"> +<h3><a name="RBM1"></a>RBM1</h3> +<p><img src="../images/example-rbm2.png" align="center" width="200px" alt="" /> <span><b>Figure 2 - RBM2.</b></span></p> +<p>Figure 2 shows the net structure of training RBM2. The visible units of RBM2 accept the output from the Sigmoid1 layer. The Inner1 layer is a <tt>InnerProductLayer</tt> whose parameters are set to the <tt>w1</tt> and <tt>b12</tt> learned from RBM1. The neural net configuration is (with layers for data layer and parser layer omitted).</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "Inner1" + type: kInnerProduct + srclayers:"mnist" + innerproduct_conf{ + num_output: 1000 + } + param{ name: "w1" } + param{ name: "b12"} +} + +layer{ + name: "Sigmoid1" + type: kSigmoid + srclayers:"Inner1" +} + +layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"Sigmoid1" + srclayers:"RBMHid" + rbm_conf{ + hdim: 500 + } + param{ + name: "w2" + ... + } + param{ + name: "b21" + ... + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 500 + } + param{ + name: "w2_" + share_from: "w2" + } + param{ + name: "b22" + ... + } +} +</pre></div></div> +<p>To load w0 and b02 from RBM0’s checkpoint file, we configure the <tt>checkpoint_path</tt> as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">checkpoint_path: "examples/rbm/rbm1/checkpoint/step6000-worker0" +cluster{ + workspace: "examples/rbm/rbm2" +} +</pre></div></div> +<p>The workspace is changed for checkpointing <tt>w2</tt>, <tt>b21</tt> and <tt>b22</tt> into <i>examples/rbm/rbm2/</i>.</p></div> +<div class="section"> +<h3><a name="RBM3"></a>RBM3</h3> +<p><img src="../images/example-rbm3.png" align="center" width="200px" alt="" /> <span><b>Figure 3 - RBM3.</b></span></p> +<p>Figure 3 shows the net structure of training RBM3. In this model, a layer with 250 units is added as the hidden layer of RBM3. The visible units of RBM3 accepts output from Sigmoid2 layer. Parameters of Inner1 and Innner2 are set to <tt>w1,b12,w2,b22</tt> which can be load from the checkpoint file of RBM2, i.e., “examples/rbm/rbm2/”.</p></div> +<div class="section"> +<h3><a name="RBM4"></a>RBM4</h3> +<p><img src="../images/example-rbm4.png" align="center" width="200px" alt="" /> <span><b>Figure 4 - RBM4.</b></span></p> +<p>Figure 4 shows the net structure of training RBM4. It is similar to Figure 3, but according to <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>, the hidden units of the top RBM (RBM4) have stochastic real-valued states drawn from a unit variance Gaussian whose mean is determined by the input from the RBM’s logistic visible units. So we add a <tt>gaussian</tt> field in the RBMHid layer to control the sampling distribution (Gaussian or Bernoulli). In addition, this RBM has a much smaller learning rate (0.001). The neural net configuration for the RBM4 and the updating protocol is (with layers for data layer and parser layer omitted),</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># Updater Configuration +updater{ + type: kSGD + momentum: 0.9 + weight_decay: 0.0002 + learning_rate{ + base_lr: 0.001 + type: kFixed + } +} + +layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"Sigmoid3" + srclayers:"RBMHid" + rbm_conf{ + hdim: 30 + } + param{ + name: "w4" + ... + } + param{ + name: "b41" + ... + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 30 + gaussian: true + } + param{ + name: "w4_" + share_from: "w4" + } + param{ + name: "b42" + ... + } +} +</pre></div></div></div> +<div class="section"> +<h3><a name="Auto-encoder"></a>Auto-encoder</h3> +<p>In the fine-tuning stage, the 4 RBMs are “unfolded” to form encoder and decoder networks that are initialized using the parameters from the previous 4 RBMs.</p> +<p><img src="../images/example-autoencoder.png" align="center" width="500px" alt="" /> <span><b>Figure 5 - Auto-Encoders.</b></span></p> +<p>Figure 5 shows the neural net structure for training the auto-encoder. <a href="train-one-batch.html">Back propagation (kBP)</a> is configured as the algorithm for <tt>TrainOneBatch</tt>. We use the same cluster configuration as RBM models. For updater, we use <a href="updater.html#adagradupdater">AdaGrad</a> algorithm with fixed learning rate.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">### Updater Configuration +updater{ + type: kAdaGrad + learning_rate{ + base_lr: 0.01 + type: kFixed + } +} +</pre></div></div> +<p>According to <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>, we configure a EuclideanLoss layer to compute the reconstruction error. The neural net configuration is (with some of the middle layers omitted),</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ name: "data" } +layer{ name:"mnist" } +layer{ + name: "Inner1" + param{ name: "w1" } + param{ name: "b12" } +} +layer{ name: "Sigmoid1" } +... +layer{ + name: "Inner8" + innerproduct_conf{ + num_output: 784 + transpose: true + } + param{ + name: "w8" + share_from: "w1" + } + param{ name: "b11" } +} +layer{ name: "Sigmoid8" } + +# Euclidean Loss Layer Configuration +layer{ + name: "loss" + type:kEuclideanLoss + srclayers:"Sigmoid8" + srclayers:"mnist" +} +</pre></div></div> +<p>To load pre-trained parameters from the 4 RBMs’ checkpoint file we configure <tt>checkpoint_path</tt> as</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">### Checkpoint Configuration +checkpoint_path: "examples/rbm/checkpoint/rbm1/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm2/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm3/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm4/checkpoint/step6000-worker0" +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Visualization_Results"></a>Visualization Results</h2> + +<div> +<img src="../images/rbm-weight.PNG" align="center" width="300px" alt="" /> + +<img src="../images/rbm-feature.PNG" align="center" width="300px" alt="" /> +<br /> +<span><b>Figure 6 - Bottom RBM weight matrix.</b></span> +  +  +  +  + +<span><b>Figure 7 - Top layer features.</b></span> +</div> +<p>Figure 6 visualizes sample columns of the weight matrix of RBM1, We can see the Gabor-like filters are learned. Figure 7 depicts the features extracted from the top-layer of the auto-encoder, wherein one point represents one image. Different colors represent different digits. We can see that most images are well clustered according to the ground truth.</p></div> + </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>
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