svn commit: r1740048 [7/10] - in /incubator/singa/site/trunk/content/markdown: ./ develop/ docs/ docs/kr/ v0.3.0/ v0.3.0/jp/ v0.3.0/kr/ v0.3.0/zh/

[wangwei]

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+# 프로그래밍 가이드
+
+---
+
+Figure 1에 그려진 다음과 같은 4가지 Components 를 설정하여 
트레이닝을 시작합니다.
+
+  * [NeuralNet](neural-net.html) : 뉴럴네트워크의 구조와 각 "ë 
ˆì´ì–´"의 설정을 기술합니다.
+  * [TrainOneBatch](train-one-batch.html) : 모델 카테고리에 적합한 
알고리즘을 기술합니다.
+  * [Updater](updater.html) : server에서 매개 변수를 업데이트하는 
방법을 기술합니다.
+  * [Cluster Topology](distributed-training.html) : workers와 servers 분산 
토폴로지를 기술합니다.
+
+*Basic 유저 가이드* 에서 built-in components 를 써서 트레이닝을 
시작하는 방법을 설명합니다. *Advanced 유저 가이드* 에서는 
유저가 임플리멘트한 모델, 함수, 알고리듬을 써서 트ë 
ˆì´ë‹ì„ 시작하는 방법을 설병합니다. 트레이닝 데이타는 
[process](data.html) 를 참고로 준비를 해주세요.
+
+<img src="../../images/overview.png" align="center" width="400px"/>
+<span><strong>Figure 1 - SINGA Overview </strong></span>
+
+
+
+## Basic 유저 가이드
+
+SINGA 에서 준비된 main 함수를 써서 쉽게 트레이닝을 시작할
수 있습니다.
+이 경우 [JobProto](../api/classsinga_1_1JobProto.html) 를 위하여 google 
protocol buffer message 로 씌여진 job configuration 파일을 
준비합니다. 그리고 아래의 커맨드라인을 실행합니다.
+
+    ./bin/singa-run.sh -conf <path to job conf> [-resume]
+
+`-resume` 는 저번 [checkpoint](checkpoint.html) 부터 다시 트ë 
ˆì´ë‹ì„ 계속할때 쓰는 인수 입니다.
+[MLP](mlp.html) 와 [CNN](cnn.html) 샘플들은 built-in 컴포넌트를 
이용하고 있습니다.
+Please read the corresponding pages for their job configuration files. The 
subsequent pages will illustrate the details on each component of the 
configuration.
+
+## Advanced 유저 가이드
+
+If a user's model contains some user-defined components, e.g.,
+[Updater](updater.html), he has to write a main function to
+register these components. It is similar to Hadoop's main function. Generally,
+the main function should
+
+* SINGA 초기화, e.g., setup logging.
+
+* 유저 컴포넌트의 등록
+
+* job configuration 을 작성하고 SINGA driver 에서 설정
+
+main 함수의 샘플입니다.
+
+    #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;
+    }
+
+Driver class' `Init` method 는 커맨드라인 인수 `-conf <job conf>` 
에서 주어진 job configuration 파일을 읽습니다. 그 파일에는 
cluster topology 정보가 기술 되어있고, 유저가 neural net, updater 
등을 업데이트 혹은 설정 하기위한 `jobConf`를 리턴합니다.
+유저가 Layer, Updater, Worker, Param 등의 subclass를 정의하면, 
driver 에 등록을 해야합니다.
+트레이닝을 시작하기 위하여 job configuration 즉 `jobConf`를 
driver.Train 에 넘겨줍니다.
+
+<!--We will provide helper functions to make the configuration easier in the
+future, like [keras](https://github.com/fchollet/keras).-->
+
+유저코드를 compile 하고 SINGA library (*.libs/libsinga.so*) 와 
링크시켜 실행파일, e.g., *mysinga*, 을 생성합니다. 
프로그램은 다음과 같이 실행합니다.
+
+    ./bin/singa-run.sh -conf <path to job conf> -exec <path to mysinga> [other 
arguments]
+
+[RNN application](rnn.html) 에서 RNN 모델의 트레이닝을 위한 
함수의 프로그램 예를 설명합니다.

Added: incubator/singa/site/trunk/content/markdown/v0.3.0/kr/quick-start.md
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+# 퀵 스타트
+
+---
+
+## SINGA 인스톨
+
+SINGA 인스톨은 [여기](installation.html)를 참조하십시오.
+
+### Zookeeper 실행
+
+SINGA 트레이닝은 [zookeeper](https://zookeeper.apache.org/)를 
이용합니다. 우선 zookeeper 서비스가 시작되어 있는지 
확인하십시오.
+
+준비된 thirdparty 스크립트를 사용하여 zookeeper를 설치 한 
경우 다음 스크립트를 실행하십시오.
+
+    #goto top level folder
+    cd SINGA_ROOT
+    ./bin/zk-service.sh start
+
+(`./bin/zk-service.sh stop` // zookeeper 중지).
+
+기본 포트를 사용하지 않고 zookeeper를 시작시킬 때는 
`conf/singa.conf`을 편집하십시오.
+
+    zookeeper_host : "localhost : YOUR_PORT"
+
+## Stand-alone 모드에서 실행
+
+Stand-alone 모드에서 SINGA을 실행할 때, 
[Mesos](http://mesos.apache.org/) 와 
[YARN](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html)
 과 같은 클러스터 관리툴을 이용하지 않는 경우를 
말합니다.
+
+### Single 노드에서의 트레이닝
+
+하나의 프로세스가 시작됩니다.
+예를 들어,
+[CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html) 데이터 세트를 
이용하여
+[CNN 
모델](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)을
 트레이닝 시킵니다.
+하이퍼 파라미터는 
[cuda-convnet](https://code.google.com/p/cuda-convnet/)에 따라 설정되어 
있습니다.
+자세한 내용은 [CNN 샘플](cnn.html) 페이지를 참조하십시오.
+
+
+#### 데이터와 작업 설정
+
+데이터 세트 다운로드와 Triaing 이나 Test 를 위한 데이터 
샤드의 생성은 다음과 같이 실시합니다.
+
+    cd examples/cifar10/
+    cp Makefile.example Makefile
+    make download
+    make create
+
+Training 과 Test 데이터 세트는 각각 *cifar10-train-shard*
+그리고 *cifar10-test-shard* 폴더에 만들어집니다. 모든 
이미지의 특징 평균을 기술한 *image_mean.bin* 파일도 함께 
생성됩니다.
+
+CNN 모델 트레이닝에 필요한 소스코드는 모두 SINGA에 
포함되어 있습니다. 코드를 추가 할 필요는 없습니다.
+작업 설정 파일(*job.conf*) 을 지정하여 
스크립트(*../../bin/singa-run.sh*)를 실행합니다.
+SINGA 코드를 변경하거나 추가 할 경우는, 프로그래밍가이드 
(programming-guide.html)를 참조하십시오.
+
+#### 병렬화 없이 트레이닝
+
+Cluster Topology의 기본값은 하나의 worker와 하나의 server가 
있습니다.
+데이터와 모델의 병렬 처리는 되지 않습니다.
+
+트레이닝을 시작하기 위하여 다음 스크립트를 실행합니다.
+
+    # goto top level folder
+    cd ../../
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+
+현재 실행중인 작업의 리스트를 보려면
+
+    ./bin/singa-console.sh list
+
+    JOB ID     | NUM PROCS
+    ---------- | -----------
+    24         | 1
+
+작업을 종료하려면
+
+    ./bin/singa-console.sh kill JOB_ID
+
+
+로그 및 작업 정보는 */tmp/singa-log* 폴더에 저장됩니다.
+*conf/singa.conf* 파일의 `log-dir`에서 변경 가능합니다.
+
+
+#### 비동기 병렬 트레이닝
+
+    # job.conf
+    ...
+    cluster {
+      nworker_groups : 2
+      nworkers_per_procs : 2
+      workspace : "examples/cifar10/"
+    }
+
+여러 worker 그룹을 실행함으로써 [비동기 트ë 
ˆì´ë‹](architecture.html)을 할 수 있습니다.
+예를 들어, *job.conf* 을 위와 같이 변경합니다.
+기본적으로 하나의 worker 그룹이 하나의 worker를 갖도록 설ì 
•ë˜ì–´ 있습니다.
+위의 설정은 하나의 프로세스에 2개의 worker가 설정되어 
있기 때문에 2개의 worker 그룹이 동일한 프로세스로 
실행됩니다.
+ê²°ê³¼ 인메모리 [Downpour](frameworks.html) 트레이닝 프ë 
ˆìž„워크로 실행됩니다.
+
+사용자는 데이터의 분산을 신경 쓸 필요는 없습니다.
+랜덤 오프셋에 따라 각 worker 그룹에 데이터가 보내집니다.
+각 worker는 다른 데이터 파티션을 담당합니다.
+
+    # job.conf
+    ...
+    neuralnet {
+      layer {
+        ...
+        sharddata_conf {
+          random_skip : 5000
+        }
+      }
+      ...
+    }
+
+스크립트 실행 :
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+#### 동기화 병렬 트레이닝
+
+    # job.conf
+    ...
+    cluster {
+      nworkers_per_group : 2
+      nworkers_per_procs : 2
+      workspace : "examples/cifar10/"
+    }
+
+하나의 worker 그룹으로 여러 worker를 실행하여 [동기 트ë 
ˆì´ë‹](architecture.html)을 수행 할 수 있습니다.
+예를 들어, *job.conf* 파일을 위와 같이 변경합니다.
+위의 설정은 하나의 worker 그룹에 2개의 worker가 설ì 
•ë˜ì—ˆìŠµë‹ˆë‹¤.
+worker 들은 그룹 내에서 동기화합니다.
+이것은 인메모리 [sandblaster](frameworks.html)로 실행됩니다.
+모델은 2개의 worker로 분할됩니다. 각 레이어가 2개의 
worker로 분산됩니다.
+배분 된 레이어는 원본 레이어와 기능은 같지만 특징 
인스턴스의 수가 `B / g` 로 됩니다.
+여기서 `B`는 미니밧치 인스턴스의 숫자로 `g`는 그룹의 
worker 수 입니다.
+[다른 스킴](neural-net.html)을 이용한 레이어 (뉴럴네트워크) 
파티션 방법도 있습니다.
+
+다른 설정들은 모두 "병렬화 없음"의 경우와 동일합니다.
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+### 클러스터에서의 트레이닝
+
+클러스터 설정을 변경하여 위 트레이닝 프레임워크를 
확장합니다.
+
+    nworker_per_procs : 1
+
+모든 프로세스는 하나의 worker 스레드를 생성합니다.
+결과 worker 우리는 다른 프로세스 (노드)에서 생성됩니다.
+클러스터의 노드를 특정하려면 *SINGA_ROOT/conf/* 의 *hostfile* 
의 설​​정이 필요합니다.
+
+e.g.,
+
+    logbase-a01
+    logbase-a02
+
+zookeeper location도 설정해야합니다.
+
+e.g.,
+
+    # conf/singa.conf
+    zookeeper_host : "logbase-a01"
+
+스크립트의 실행은 "Single 노드 트레이닝"과 동일합니다.
+
+    ./bin/singa-run.sh -conf examples/cifar10/job.conf
+
+## Mesos에서 실행
+
+*working* ...
+
+## 다음
+
+SINGA 의 코드 변경 및 추가에 대한 자세한 내용은 
[프로그래밍 가이드](programming-guide.html)를 참조하십시오.

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+# RBM Example
+
+---
+
+This example uses SINGA to train 4 RBM models and one auto-encoder model over 
the
+[MNIST dataset](http://yann.lecun.com/exdb/mnist/). 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 [Hinton's science paper](http://www.cs.toronto.edu/~hinton/science.pdf).
+
+## Running instructions
+
+Running scripts are provided in *SINGA_ROOT/examples/rbm* folder.
+
+The MNIST dataset has 70,000 handwritten digit images. The
+[data preparation](data.html) page
+has details on converting this dataset into SINGA recognizable format. Users 
can
+simply run the following commands to download and convert the dataset.
+
+    # at SINGA_ROOT/examples/mnist/
+    $ cp Makefile.example Makefile
+    $ make download
+    $ make create
+
+The training is separated into two phases, namely pre-training and fine-tuning.
+The pre-training phase trains 4 RBMs in sequence,
+
+    # 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
+
+The fine-tuning phase trains the auto-encoder by,
+
+    $ ./bin/singa-run.sh -conf examples/rbm/autoencoder.conf
+
+
+## Training details
+
+### RBM1
+
+<img src="../images/example-rbm1.png" align="center" width="200px"/>
+<span><strong>Figure 1 - RBM1.</strong></span>
+
+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,
+
+    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
+        }
+      }
+    }
+
+
+
+For RBM, the weight matrix is shared by the visible and hidden layers. For 
instance,
+`w1` is shared by `vis` and `hid` layers shown in Figure 1. In SINGA, we can 
configure
+the `share_from` field to enable [parameter sharing](param.html)
+as shown above for the param `w1` and `w1_`.
+
+[Contrastive Divergence](train-one-batch.html#contrastive-divergence)
+is configured as the algorithm for [TrainOneBatch](train-one-batch.html).
+Following Hinton's paper, we configure the [updating protocol](updater.html)
+as follows,
+
+    # Updater Configuration
+    updater{
+      type: kSGD
+      momentum: 0.2
+      weight_decay: 0.0002
+      learning_rate{
+        base_lr: 0.1
+        type: kFixed
+      }
+    }
+
+Since the parameters of RBM0 will be used to initialize the auto-encoder, we 
should
+configure the `workspace` field to specify a path for the checkpoint folder.
+For example, if we configure it as,
+
+    cluster {
+      workspace: "examples/rbm/rbm1/"
+    }
+
+Then SINGA will [checkpoint the parameters](checkpoint.html) into 
*examples/rbm/rbm1/*.
+
+### RBM1
+<img src="../images/example-rbm2.png" align="center" width="200px"/>
+<span><strong>Figure 2 - RBM2.</strong></span>
+
+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  `InnerProductLayer` whose parameters are set to the `w1` and `b12` 
learned
+from RBM1.
+The neural net configuration is (with layers for data layer and parser layer 
omitted).
+
+    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"
+        ...
+      }
+    }
+
+To load w0 and b02 from RBM0's checkpoint file, we configure the 
`checkpoint_path` as,
+
+    checkpoint_path: "examples/rbm/rbm1/checkpoint/step6000-worker0"
+    cluster{
+      workspace: "examples/rbm/rbm2"
+    }
+
+The workspace is changed for checkpointing `w2`, `b21` and `b22` into
+*examples/rbm/rbm2/*.
+
+### RBM3
+
+<img src="../images/example-rbm3.png" align="center" width="200px"/>
+<span><strong>Figure 3 - RBM3.</strong></span>
+
+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
+`w1,b12,w2,b22` which can be load from the checkpoint file of RBM2,
+i.e., "examples/rbm/rbm2/".
+
+### RBM4
+
+
+<img src="../images/example-rbm4.png" align="center" width="200px"/>
+<span><strong>Figure 4 - RBM4.</strong></span>
+
+Figure 4 shows the net structure of training RBM4. It is similar to Figure 3,
+but according to [Hinton's science 
paper](http://www.cs.toronto.edu/~hinton/science.pdf), 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 `gaussian` 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),
+
+    # 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"
+        ...
+      }
+    }
+
+### Auto-encoder
+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.
+
+<img src="../images/example-autoencoder.png" align="center" width="500px"/>
+<span><strong>Figure 5 - Auto-Encoders.</strong></span>
+
+
+Figure 5 shows the neural net structure for training the auto-encoder.
+[Back propagation (kBP)] (train-one-batch.html) is
+configured as the algorithm for `TrainOneBatch`. We use the same cluster
+configuration as RBM models. For updater, we use 
[AdaGrad](updater.html#adagradupdater) algorithm with
+fixed learning rate.
+
+    ### Updater Configuration
+    updater{
+      type: kAdaGrad
+      learning_rate{
+      base_lr: 0.01
+      type: kFixed
+      }
+    }
+
+
+
+According to [Hinton's science 
paper](http://www.cs.toronto.edu/~hinton/science.pdf),
+we configure a EuclideanLoss layer to compute the reconstruction error. The 
neural net
+configuration is (with some of the middle layers omitted),
+
+    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"
+    }
+
+To load pre-trained parameters from the 4 RBMs' checkpoint file we configure 
`checkpoint_path` as
+
+    ### 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"
+
+
+## Visualization Results
+
+<div>
+<img src="../images/rbm-weight.PNG" align="center" width="300px"/>
+
+<img src="../images/rbm-feature.PNG" align="center" width="300px"/>
+<br/>
+<span><strong>Figure 6 - Bottom RBM weight matrix.</strong></span>
+&nbsp;
+&nbsp;
+&nbsp;
+&nbsp;
+
+<span><strong>Figure 7 - Top layer features.</strong></span>
+</div>
+
+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.

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+# Recurrent Neural Networks for Language Modelling
+
+---
+
+Recurrent Neural Networks (RNN) are widely used for modelling sequential data,
+such as music and sentences.  In this example, we use SINGA to train a
+[RNN 
model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf)
+proposed by Tomas Mikolov for [language 
modeling](https://en.wikipedia.org/wiki/Language_model).
+The training objective (loss) is
+to minimize the [perplexity per 
word](https://en.wikipedia.org/wiki/Perplexity), which
+is equivalent to maximize the probability of predicting the next word given 
the current word in
+a sentence.
+
+Different to the [CNN](cnn.html), [MLP](mlp.html)
+and [RBM](rbm.html) examples which use built-in
+layers(layer) and records(data),
+none of the layers in this example are built-in. Hence users would learn to
+implement their own layers and data records through this example.
+
+## Running instructions
+
+In *SINGA_ROOT/examples/rnnlm/*, scripts are provided to run the training job.
+First, the data is prepared by
+
+    $ cp Makefile.example Makefile
+    $ make download
+    $ make create
+
+Second, to compile the source code under *examples/rnnlm/*, run
+
+    $ make rnnlm
+
+An executable file *rnnlm.bin* will be generated.
+
+Third, the training is started by passing *rnnlm.bin* and the job configuration
+to *singa-run.sh*,
+
+    # at SINGA_ROOT/
+    # export LD_LIBRARY_PATH=.libs:$LD_LIBRARY_PATH
+    $ ./bin/singa-run.sh -exec examples/rnnlm/rnnlm.bin -conf 
examples/rnnlm/job.conf
+
+## Implementations
+
+<img src="../images/rnnlm.png" align="center" width="400px"/>
+<span><strong>Figure 1 - Net structure of the RNN model.</strong></span>
+
+The neural net structure is shown Figure 1.  Word records are loaded by
+`DataLayer`. For every iteration, at most `max_window` word records are
+processed. If a sentence ending character is read, the `DataLayer` stops
+loading immediately. `EmbeddingLayer` looks up a word embedding matrix to 
extract
+feature vectors for words loaded by the `DataLayer`.  These features are 
transformed by the
+`HiddenLayer` which propagates the features from left to right. The
+output feature for word at position k is influenced by words from position 0 to
+k-1.  Finally, `LossLayer` computes the cross-entropy loss (see below)
+by predicting the next word of each word.
+The cross-entropy loss is computed as
+
+`$$L(w_t)=-log P(w_{t+1}|w_t)$$`
+
+Given `$w_t$` the above equation would compute over all words in the 
vocabulary,
+which is time consuming.
+[RNNLM 
Toolkit](https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz)
+accelerates the computation as
+
+`$$P(w_{t+1}|w_t) = P(C_{w_{t+1}}|w_t) * P(w_{t+1}|C_{w_{t+1}})$$`
+
+Words from the vocabulary are partitioned into a user-defined number of 
classes.
+The first term on the left side predicts the class of the next word, and
+then predicts the next word given its class. Both the number of classes and
+the words from one class are much smaller than the vocabulary size. The 
probabilities
+can be calculated much faster.
+
+The perplexity per word is computed by,
+
+`$$PPL = 10^{- avg_t log_{10} P(w_{t+1}|w_t)}$$`
+
+### Data preparation
+
+We use a small dataset provided by the [RNNLM 
Toolkit](https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz).
+It has 10,000 training sentences, with 71350 words in total and 3720 unique 
words.
+The subsequent steps follow the instructions in
+[Data Preparation](data.html) to convert the
+raw data into records and insert them into data stores.
+
+#### Download source data
+
+    # in SINGA_ROOT/examples/rnnlm/
+    cp Makefile.example Makefile
+    make download
+
+#### Define record format
+
+We define the word record as follows,
+
+    # in SINGA_ROOT/examples/rnnlm/rnnlm.proto
+    message WordRecord {
+      optional string word = 1;
+      optional int32 word_index = 2;
+      optional int32 class_index = 3;
+      optional int32 class_start = 4;
+      optional int32 class_end = 5;
+    }
+
+It includes the word string and its index in the vocabulary.
+Words in the vocabulary are sorted based on their frequency in the training 
dataset.
+The sorted list is cut into 100 sublists such that each sublist has 1/100 total
+word frequency. Each sublist is called a class.
+Hence each word has a `class_index` ([0,100)). The `class_start` is the index
+of the first word in the same class as `word`. The `class_end` is the index of
+the first word in the next class.
+
+#### Create data stores
+
+We use code from RNNLM Toolkit to read words, and sort them into classes.
+The main function in *create_store.cc* first creates word classes based on the 
training
+dataset. Second it calls the following function to create data store for the
+training, validation and test dataset.
+
+    int create_data(const char *input_file, const char *output_file);
+
+`input` is the path to training/validation/testing text file from the RNNLM 
Toolkit, `output` is output store file.
+This function starts with
+
+    singa::io::KVFile store;
+    store.Open(output, signa::io::kCreate);
+
+Then it reads the words one by one. For each word it creates a `WordRecord` 
instance,
+and inserts it into the store,
+
+    int wcnt = 0; // word count
+    WordRecord  wordRecord;
+    while(1) {
+      readWord(wordstr, fin);
+      if (feof(fin)) break;
+      ...// fill in the wordRecord;
+      string val;
+      wordRecord.SerializeToString(&val);
+      int length = snprintf(key, BUFFER_LEN, "%05d", wcnt++);
+      store.Write(string(key, length), val);
+    }
+
+Compilation and running commands are provided in the *Makefile.example*.
+After executing
+
+    make create
+
+*train_data.bin*, *test_data.bin* and *valid_data.bin* will be created.
+
+
+### Layer implementation
+
+4 user-defined layers are implemented for this application.
+Following the guide for implementing [new Layer 
subclasses](layer#implementing-a-new-layer-subclass),
+we extend the [LayerProto](../api/classsinga_1_1LayerProto.html)
+to include the configuration messages of user-defined layers as shown below
+(3 out of the 7 layers have specific configurations),
+
+
+    import "job.proto";     // Layer message for SINGA is defined
+
+    //For implementation of RNNLM application
+    extend singa.LayerProto {
+      optional EmbeddingProto embedding_conf = 101;
+      optional LossProto loss_conf = 102;
+      optional DataProto data_conf = 103;
+    }
+
+In the subsequent sections, we describe the implementation of each layer,
+including its configuration message.
+
+#### RNNLayer
+
+This is the base layer of all other layers for this applications. It is defined
+as follows,
+
+    class RNNLayer : virtual public Layer {
+    public:
+      inline int window() { return window_; }
+    protected:
+      int window_;
+    };
+
+For this application, two iterations may process different number of words.
+Because sentences have different lengths.
+The `DataLayer` decides the effective window size. All other layers call its 
source layers to get the
+effective window size and resets `window_` in `ComputeFeature` function.
+
+#### DataLayer
+
+DataLayer is for loading Records.
+
+    class DataLayer : public RNNLayer, singa::InputLayer {
+     public:
+      void Setup(const LayerProto& proto, const vector<Layer*>& srclayers) 
override;
+      void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
+      int max_window() const {
+        return max_window_;
+      }
+     private:
+      int max_window_;
+      singa::io::Store* store_;
+    };
+
+The Setup function gets the user configured max window size.
+
+    max_window_ = proto.GetExtension(input_conf).max_window();
+
+The `ComputeFeature` function loads at most max_window records. It could also
+stop when the sentence ending character is encountered.
+
+    ...// shift the last record to the first
+    window_ = max_window_;
+    for (int i = 1; i <= max_window_; i++) {
+      // load record; break if it is the ending character
+    }
+
+The configuration of `DataLayer` is like
+
+    name: "data"
+    user_type: "kData"
+    [data_conf] {
+      path: "examples/rnnlm/train_data.bin"
+      max_window: 10
+    }
+
+#### EmbeddingLayer
+
+This layer gets records from `DataLayer`. For each record, the word index is
+parsed and used to get the corresponding word feature vector from the embedding
+matrix.
+
+The class is declared as follows,
+
+    class EmbeddingLayer : public RNNLayer {
+      ...
+      const std::vector<Param*> GetParams() const override {
+        std::vector<Param*> params{embed_};
+        return params;
+      }
+     private:
+      int word_dim_, vocab_size_;
+      Param* embed_;
+    }
+
+The `embed_` field is a matrix whose values are parameter to be learned.
+The matrix size is `vocab_size_` x `word_dim_`.
+
+The Setup function reads configurations for `word_dim_` and `vocab_size_`. Then
+it allocates feature Blob for `max_window` words and setups `embed_`.
+
+    int max_window = srclayers[0]->data(this).shape()[0];
+    word_dim_ = proto.GetExtension(embedding_conf).word_dim();
+    data_.Reshape(vector<int>{max_window, word_dim_});
+    ...
+    embed_->Setup(vector<int>{vocab_size_, word_dim_});
+
+The `ComputeFeature` function simply copies the feature vector from the 
`embed_`
+matrix into the feature Blob.
+
+    # reset effective window size
+    window_ = datalayer->window();
+    auto records = datalayer->records();
+    ...
+    for (int t = 0; t < window_; t++) {
+      int idx  <- word index
+      Copy(words[t], embed[idx]);
+    }
+
+The `ComputeGradient` function copies back the gradients to the `embed_` 
matrix.
+
+The configuration for `EmbeddingLayer` is like,
+
+    user_type: "kEmbedding"
+    [embedding_conf] {
+      word_dim: 15
+      vocab_size: 3720
+    }
+    srclayers: "data"
+    param {
+      name: "w1"
+      init {
+        type: kUniform
+        low:-0.3
+        high:0.3
+      }
+    }
+
+#### HiddenLayer
+
+This layer unrolls the recurrent connections for at most max_window times.
+The feature for position k is computed based on the feature from the embedding 
layer (position k)
+and the feature at position k-1 of this layer. The formula is
+
+`$$f[k]=\sigma (f[t-1]*W+src[t])$$`
+
+where `$W$` is a matrix with `word_dim_` x `word_dim_` parameters.
+
+If you want to implement a recurrent neural network following our
+design, this layer is of vital importance for you to refer to.
+
+    class HiddenLayer : public RNNLayer {
+      ...
+      const std::vector<Param*> GetParams() const override {
+        std::vector<Param*> params{weight_};
+        return params;
+      }
+    private:
+      Param* weight_;
+    };
+
+The `Setup` function setups the weight matrix as
+
+    weight_->Setup(std::vector<int>{word_dim, word_dim});
+
+The `ComputeFeature` function gets the effective window size (`window_`) from 
its source layer
+i.e., the embedding layer. Then it propagates the feature from position 0 to 
position
+`window_` -1. The detailed descriptions for this process are illustrated as 
follows.
+
+    void HiddenLayer::ComputeFeature() {
+      for(int t = 0; t < window_size; t++){
+        if(t == 0)
+          Copy(data[t], src[t]);
+        else
+          data[t]=sigmoid(data[t-1]*W + src[t]);
+      }
+    }
+
+The `ComputeGradient` function computes the gradient of the loss w.r.t. W and 
the source layer.
+Particularly, for each position k, since data[k] contributes to data[k+1] and 
the feature
+at position k in its destination layer (the loss layer), grad[k] should 
contains the gradient
+from two parts. The destination layer has already computed the gradient from 
the loss layer into
+grad[k]; In the `ComputeGradient` function, we need to add the gradient from 
position k+1.
+
+    void HiddenLayer::ComputeGradient(){
+      ...
+      for (int k = window_ - 1; k >= 0; k--) {
+        if (k < window_ - 1) {
+          grad[k] += dot(grad[k + 1], weight.T()); // add gradient from 
position t+1.
+        }
+        grad[k] =... // compute gL/gy[t], y[t]=data[t-1]*W+src[t]
+      }
+      gweight = dot(data.Slice(0, window_-1).T(), grad.Slice(1, window_));
+      Copy(gsrc, grad);
+    }
+
+After the loop, we get the gradient of the loss w.r.t y[k], which is used to
+compute the gradient of W and the src[k].
+
+#### LossLayer
+
+This layer computes the cross-entropy loss and the `$log_{10}P(w_{t+1}|w_t)$` 
(which
+could be averaged over all words by users to get the PPL value).
+
+There are two configuration fields to be specified by users.
+
+    message LossProto {
+      optional int32 nclass = 1;
+      optional int32 vocab_size = 2;
+    }
+
+There are two weight matrices to be learned
+
+    class LossLayer : public RNNLayer {
+      ...
+     private:
+      Param* word_weight_, *class_weight_;
+    }
+
+The ComputeFeature function computes the two probabilities respectively.
+
+`$$P(C_{w_{t+1}}|w_t) = Softmax(w_t * class\_weight_)$$`
+`$$P(w_{t+1}|C_{w_{t+1}}) = Softmax(w_t * 
word\_weight[class\_start:class\_end])$$`
+
+`$w_t$` is the feature from the hidden layer for the k-th word, its ground 
truth
+next word is `$w_{t+1}$`.  The first equation computes the probability 
distribution over all
+classes for the next word. The second equation computes the
+probability distribution over the words in the ground truth class for the next 
word.
+
+The ComputeGradient function computes the gradient of the source layer
+(i.e., the hidden layer) and the two weight matrices.
+
+### Updater Configuration
+
+We employ kFixedStep type of the learning rate change method and the
+configuration is as follows. We decay the learning rate once the performance 
does
+not increase on the validation dataset.
+
+    updater{
+      type: kSGD
+      learning_rate {
+        type: kFixedStep
+        fixedstep_conf:{
+          step:0
+          step:48810
+          step:56945
+          step:65080
+          step:73215
+          step_lr:0.1
+          step_lr:0.05
+          step_lr:0.025
+          step_lr:0.0125
+          step_lr:0.00625
+        }
+      }
+    }
+
+### TrainOneBatch() Function
+
+We use BP (BackPropagation) algorithm to train the RNN model here. The
+corresponding configuration can be seen below.
+
+    # In job.conf file
+    train_one_batch {
+      alg: kBackPropagation
+    }
+
+### Cluster Configuration
+
+The default cluster configuration can be used, i.e., single worker and single 
server
+in a single process.

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+# Performance Test and Feature Extraction
+
+----
+
+Once SINGA finishes the training of a model, it would checkpoint the model 
parameters
+into disk files under the [checkpoint folder](checkpoint.html). Model 
parameters can also be dumped
+into this folder periodically during training if the
+[checkpoint configuration[(checkpoint.html) fields are set. With the checkpoint
+files, we can load the model parameters to conduct performance test, feature 
extraction and prediction
+against new data.
+
+To load the model parameters from checkpoint files, we need to add the paths of
+checkpoint files in the job configuration file
+
+    checkpoint_path: PATH_TO_CHECKPOINT_FILE1
+    checkpoint_path: PATH_TO_CHECKPOINT_FILE2
+    ...
+
+The new dataset is configured by specifying the ``test_step`` and the data 
input
+layer, e.g. the following configuration is for a dataset with 100*100 
instances.
+
+    test_steps: 100
+    net {
+      layer {
+        name: "input"
+        store_conf {
+          backend: "kvfile"
+          path: PATH_TO_TEST_KVFILE
+          batchsize: 100
+        }
+      }
+      ...
+    }
+
+## Performance Test
+
+This application is to test the performance, e.g., accuracy, of the previously
+trained model. Depending on the application, the test data may have ground 
truth
+labels or not. For example, if the model is trained for image classification,
+the test images must have ground truth labels to calculate the accuracy; if the
+model is an auto-encoder, the performance could be measured by reconstruction 
error, which
+does not require extra labels. For both cases, there would be a layer that 
calculates
+the performance, e.g., the `SoftmaxLossLayer`.
+
+The job configuration file for the cifar10 example can be used directly for 
testing after
+adding the checkpoint path. The running command is
+
+
+    $ ./bin/singa-run.sh -conf examples/cifar10/job.conf -test
+
+The performance would be output on the screen like,
+
+
+    Load from checkpoint file examples/cifar10/checkpoint/step50000-worker0
+    accuracy = 0.728000, loss = 0.807645
+
+## Feature extraction
+
+Since deep learning models are good at learning features, feature extraction 
for
+is a major functionality of deep learning models, e.g., we can extract features
+from the fully connected layers of 
[AlexNet](www.cs.toronto.edu/~fritz/absps/imagenet.pdf) as image features for 
image retrieval.
+To extract the features from one layer, we simply add an output layer after 
that layer.
+For instance, to extract the fully connected (with name `ip1`) layer of the 
cifar10 example model,
+we replace the `SoftmaxLossLayer` with a `CSVOutputLayer` which extracts the 
features into a CSV file,
+
+    layer {
+      name: "ip1"
+    }
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers: "ip1"
+      store_conf {
+        backend: "textfile"
+        path: OUTPUT_FILE_PATH
+      }
+    }
+
+The input layer and test steps, and the running command are the same as in 
*Performance Test* section.
+
+## Label Prediction
+
+If the output layer is connected to a layer that predicts labels of images,
+the output layer would then write the prediction results into files.
+SINGA provides two built-in layers for generating prediction results, namely,
+
+* SoftmaxLayer, generates probabilities of each candidate labels.
+* ArgSortLayer, sorts labels according to probabilities in descending order 
and keep topk labels.
+
+By connecting the two layers with the previous layer and the output layer, we 
can
+extract the predictions of each instance. For example,
+
+    layer {
+      name: "feature"
+      ...
+    }
+    layer {
+      name: "softmax"
+      type: kSoftmax
+      srclayers: "feature"
+    }
+    layer {
+      name: "prediction"
+      type: kArgSort
+      srclayers: "softmax"
+      argsort_conf {
+        topk: 5
+      }
+    }
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers: "prediction"
+      store_conf {}
+    }
+
+The top-5 labels of each instance will be written as one line of the output 
CSV file.
+Currently, above layers cannot co-exist with the loss layers used for training.
+Please comment out the loss layers for extracting prediction results.

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+# Train-One-Batch
+
+---
+
+For each SGD iteration, every worker calls the `TrainOneBatch` function to
+compute gradients of parameters associated with local layers (i.e., layers
+dispatched to it).  SINGA has implemented two algorithms for the
+`TrainOneBatch` function. Users select the corresponding algorithm for
+their model in the configuration.
+
+## Basic user guide
+
+### Back-propagation
+
+[BP algorithm](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) is used for
+computing gradients of feed-forward models, e.g., [CNN](cnn.html)
+and [MLP](mlp.html), and [RNN](rnn.html) models in SINGA.
+
+
+    # in job.conf
+    alg: kBP
+
+To use the BP algorithm for the `TrainOneBatch` function, users just simply
+configure the `alg` field with `kBP`. If a neural net contains user-defined
+layers, these layers must be implemented properly be to consistent with the
+implementation of the BP algorithm in SINGA (see below).
+
+
+### Contrastive Divergence
+
+[CD algorithm](http://www.cs.toronto.edu/~fritz/absps/nccd.pdf) is used for
+computing gradients of energy models like RBM.
+
+    # job.conf
+    alg: kCD
+    cd_conf {
+      cd_k: 2
+    }
+
+To use the CD algorithm for the `TrainOneBatch` function, users just configure
+the `alg` field to `kCD`. Uses can also configure the Gibbs sampling steps in
+the CD algorthm through the `cd_k` field. By default, it is set to 1.
+
+
+
+## Advanced user guide
+
+### Implementation of BP
+
+The BP algorithm is implemented in SINGA following the below pseudo code,
+
+    BPTrainOnebatch(step, net) {
+      // forward propagate
+      foreach layer in net.local_layers() {
+        if IsBridgeDstLayer(layer)
+          recv data from the src layer (i.e., BridgeSrcLayer)
+        foreach param in layer.params()
+          Collect(param) // recv response from servers for last update
+
+        layer.ComputeFeature(kForward)
+
+        if IsBridgeSrcLayer(layer)
+          send layer.data_ to dst layer
+      }
+      // backward propagate
+      foreach layer in reverse(net.local_layers) {
+        if IsBridgeSrcLayer(layer)
+          recv gradient from the dst layer (i.e., BridgeDstLayer)
+          recv response from servers for last update
+
+        layer.ComputeGradient()
+        foreach param in layer.params()
+          Update(step, param) // send param.grad_ to servers
+
+        if IsBridgeDstLayer(layer)
+          send layer.grad_ to src layer
+      }
+    }
+
+
+It forwards features through all local layers (can be checked by layer
+partition ID and worker ID) and backwards gradients in the reverse order.
+[BridgeSrcLayer](layer.html#bridgesrclayer--bridgedstlayer)
+(resp. `BridgeDstLayer`) will be blocked until the feature (resp.
+gradient) from the source (resp. destination) layer comes. Parameter gradients
+are sent to servers via `Update` function. Updated parameters are collected via
+`Collect` function, which will be blocked until the parameter is updated.
+[Param](param.html) objects have versions, which can be used to
+check whether the `Param` objects have been updated or not.
+
+Since RNN models are unrolled into feed-forward models, users need to implement
+the forward propagation in the recurrent layer's `ComputeFeature` function,
+and implement the backward propagation in the recurrent layer's 
`ComputeGradient`
+function. As a result, the whole `TrainOneBatch` runs
+[back-propagation through time 
(BPTT)](https://en.wikipedia.org/wiki/Backpropagation_through_time)  algorithm.
+
+### Implementation of CD
+
+The CD algorithm is implemented in SINGA following the below pseudo code,
+
+    CDTrainOneBatch(step, net) {
+      # positive phase
+      foreach layer in net.local_layers()
+        if IsBridgeDstLayer(layer)
+          recv positive phase data from the src layer (i.e., BridgeSrcLayer)
+        foreach param in layer.params()
+          Collect(param)  // recv response from servers for last update
+        layer.ComputeFeature(kPositive)
+        if IsBridgeSrcLayer(layer)
+          send positive phase data to dst layer
+
+      # negative phase
+      foreach gibbs in [0...layer_proto_.cd_k]
+        foreach layer in net.local_layers()
+          if IsBridgeDstLayer(layer)
+            recv negative phase data from the src layer (i.e., BridgeSrcLayer)
+          layer.ComputeFeature(kPositive)
+          if IsBridgeSrcLayer(layer)
+            send negative phase data to dst layer
+
+      foreach layer in net.local_layers()
+        layer.ComputeGradient()
+        foreach param in layer.params
+          Update(param)
+    }
+
+Parameter gradients are computed after the positive phase and negative phase.
+
+### Implementing a new algorithm
+
+SINGA implements BP and CD by creating two subclasses of
+the [Worker](../api/classsinga_1_1Worker.html) class:
+[BPWorker](../api/classsinga_1_1BPWorker.html)'s `TrainOneBatch` function 
implements the BP
+algorithm; [CDWorker](../api/classsinga_1_1CDWorker.html)'s `TrainOneBatch` 
function implements the CD
+algorithm. To implement a new algorithm for the `TrainOneBatch` function, users
+need to create a new subclass of the `Worker`, e.g.,
+
+    class FooWorker : public Worker {
+      void TrainOneBatch(int step, shared_ptr<NeuralNet> net, Metric* perf) 
override;
+      void TestOneBatch(int step, Phase phase, shared_ptr<NeuralNet> net, 
Metric* perf) override;
+    };
+
+The `FooWorker` must implement the above two functions for training one
+mini-batch and testing one mini-batch. The `perf` argument is for collecting
+training or testing performance, e.g., the objective loss or accuracy. It is
+passed to the `ComputeFeature` function of each layer.
+
+Users can define some fields for users to configure
+
+    # in user.proto
+    message FooWorkerProto {
+      optional int32 b = 1;
+    }
+
+    extend JobProto {
+      optional FooWorkerProto foo_conf = 101;
+    }
+
+    # in job.proto
+    JobProto {
+      ...
+      extension 101..max;
+    }
+
+It is similar as [adding configuration fields for a new 
layer](layer.html#implementing-a-new-layer-subclass).
+
+To use `FooWorker`, users need to register it in the 
[main.cc](programming-guide.html)
+and configure the `alg` and `foo_conf` fields,
+
+    # in main.cc
+    const int kFoo = 3; // worker ID, must be different to that of CDWorker 
and BPWorker
+    driver.RegisterWorker<FooWorker>(kFoo);
+
+    # in job.conf
+    ...
+    alg: 3
+    [foo_conf] {
+      b = 4;
+    }

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+# Updater
+
+---
+
+Every server in SINGA has an [Updater](../api/classsinga_1_1Updater.html)
+instance that updates parameters based on gradients.
+In this page, the *Basic user guide* describes the configuration of an updater.
+The *Advanced user guide* present details on how to implement a new updater 
and a new
+learning rate changing method.
+
+## Basic user guide
+
+There are many different parameter updating protocols (i.e., subclasses of
+`Updater`). They share some configuration fields like
+
+* `type`, an integer for identifying an updater;
+* `learning_rate`, configuration for the
+[LRGenerator](../api/classsinga_1_1LRGenerator.html) which controls the 
learning rate.
+* `weight_decay`, the co-efficient for [L2 * 
regularization](http://deeplearning.net/tutorial/gettingstarted.html#regularization).
+* 
[momentum](http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/).
+
+If you are not familiar with the above terms, you can get their meanings in
+[this page provided by 
Karpathy](http://cs231n.github.io/neural-networks-3/#update).
+
+### Configuration of built-in updater classes
+
+#### Updater
+The base `Updater` implements the [vanilla SGD 
algorithm](http://cs231n.github.io/neural-networks-3/#sgd).
+Its configuration type is `kSGD`.
+Users need to configure at least the `learning_rate` field.
+`momentum` and `weight_decay` are optional fields.
+
+    updater{
+      type: kSGD
+      momentum: float
+      weight_decay: float
+      learning_rate {
+        ...
+      }
+    }
+
+#### AdaGradUpdater
+
+It inherits the base `Updater` to implement the
+[AdaGrad](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf) algorithm.
+Its type is `kAdaGrad`.
+`AdaGradUpdater` is configured similar to `Updater` except
+that `momentum` is not used.
+
+#### NesterovUpdater
+
+It inherits the base `Updater` to implements the
+[Nesterov](http://arxiv.org/pdf/1212.0901v2.pdf) (section 3.5) updating 
protocol.
+Its type is `kNesterov`.
+`learning_rate` and `momentum` must be configured. `weight_decay` is an
+optional configuration field.
+
+#### RMSPropUpdater
+
+It inherits the base `Updater` to implements the
+[RMSProp algorithm](http://cs231n.github.io/neural-networks-3/#sgd) proposed by
+[Hinton](http://www.cs.toronto.edu/%7Etijmen/csc321/slides/lecture_slides_lec6.pdf)(slide
 29).
+Its type is `kRMSProp`.
+
+    updater {
+      type: kRMSProp
+      rmsprop_conf {
+       rho: float # [0,1]
+      }
+    }
+
+
+### Configuration of learning rate
+
+The `learning_rate` field is configured as,
+
+    learning_rate {
+      type: ChangeMethod
+      base_lr: float  # base/initial learning rate
+      ... # fields to a specific changing method
+    }
+
+The common fields include `type` and `base_lr`. SINGA provides the following
+`ChangeMethod`s.
+
+#### kFixed
+
+The `base_lr` is used for all steps.
+
+#### kLinear
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr:  float
+      linear_conf {
+        freq: int
+        final_lr: float
+      }
+    }
+
+Linear interpolation is used to change the learning rate,
+
+    lr = (1 - step / freq) * base_lr + (step / freq) * final_lr
+
+#### kExponential
+
+The udapter should be configured like
+
+    learning_rate {
+      base_lr: float
+      exponential_conf {
+        freq: int
+      }
+    }
+
+The learning rate for `step` is
+
+    lr = base_lr / 2^(step / freq)
+
+#### kInverseT
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr: float
+      inverset_conf {
+        final_lr: float
+      }
+    }
+
+The learning rate for `step` is
+
+    lr = base_lr / (1 + step / final_lr)
+
+#### kInverse
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr: float
+      inverse_conf {
+        gamma: float
+        pow: float
+      }
+    }
+
+
+The learning rate for `step` is
+
+    lr = base_lr * (1 + gamma * setp)^(-pow)
+
+
+#### kStep
+
+The updater should be configured like
+
+    learning_rate {
+      base_lr : float
+      step_conf {
+        change_freq: int
+        gamma: float
+      }
+    }
+
+
+The learning rate for `step` is
+
+    lr = base_lr * gamma^ (step / change_freq)
+
+#### kFixedStep
+
+The updater should be configured like
+
+    learning_rate {
+      fixedstep_conf {
+        step: int
+        step_lr: float
+
+        step: int
+        step_lr: float
+
+        ...
+      }
+    }
+
+Denote the i-th tuple as (step[i], step_lr[i]), then the learning rate for
+`step` is,
+
+    step_lr[k]
+
+where step[k] is the smallest number that is larger than `step`.
+
+
+## Advanced user guide
+
+### Implementing a new Updater subclass
+
+The base Updater class has one virtual function,
+
+    class Updater{
+     public:
+      virtual void Update(int step, Param* param, float grad_scale = 1.0f) = 0;
+
+     protected:
+      UpdaterProto proto_;
+      LRGenerator lr_gen_;
+    };
+
+It updates the values of the `param` based on its gradients. The `step`
+argument is for deciding the learning rate which may change through time
+(step). `grad_scale` scales the original gradient values. This function is
+called by servers once it receives all gradients for the same `Param` object.
+
+To implement a new Updater subclass, users must override the `Update` function.
+
+    class FooUpdater : public Updater {
+      void Update(int step, Param* param, float grad_scale = 1.0f) override;
+    };
+
+Configuration of this new updater can be declared similar to that of a new
+layer,
+
+    # in user.proto
+    FooUpdaterProto {
+      optional int32 c = 1;
+    }
+
+    extend UpdaterProto {
+      optional FooUpdaterProto fooupdater_conf= 101;
+    }
+
+The new updater should be registered in the
+[main function](programming-guide.html)
+
+    driver.RegisterUpdater<FooUpdater>("FooUpdater");
+
+Users can then configure the job as
+
+    # 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;
+      }
+    }
+
+### Implementing a new LRGenerator subclass
+
+The base `LRGenerator` is declared as,
+
+    virtual float Get(int step);
+
+To implement a subclass, e.g., `FooLRGen`, users should declare it like
+
+    class FooLRGen : public LRGenerator {
+     public:
+      float Get(int step) override;
+    };
+
+Configuration of `FooLRGen` can be defined using a protocol message,
+
+    # in user.proto
+    message FooLRProto {
+     ...
+    }
+
+    extend LRGenProto {
+      optional FooLRProto foolr_conf = 101;
+    }
+
+The configuration is then like,
+
+    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 {
+        ...
+      }
+    }
+
+Users have to register this subclass in the main function,
+
+      driver.RegisterLRGenerator<FooLRGen, std::string>("FooLR")

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+# Layers
+
+---
+
+Layer is a core abstraction in SINGA. It performs a variety of feature
+transformations for extracting high-level features, e.g., loading raw features,
+parsing RGB values, doing convolution transformation, etc.
+
+The *Basic user guide* section introduces the configuration of a built-in
+layer. *Advanced user guide* explains how to extend the base Layer class to
+implement users' functions.
+
+## Basic user guide
+
+### Layer configuration
+
+Configuration of two example layers are shown below,
+
+    layer {
+      name: "data"
+      type: kCSVRecord
+      store_conf { }
+    }
+    layer{
+      name: "fc1"
+      type: kInnerProduct
+      srclayers: "data"
+      innerproduct_conf{ }
+      param{ }
+    }
+
+There are some common fields for all kinds of layers:
+
+  * `name`: a string used to differentiate two layers in a neural net.
+  * `type`: an integer used for identifying a specific Layer subclass. The 
types of built-in
+  layers are listed in LayerType (defined in job.proto).
+  For user-defined layer subclasses, `user_type` should be used instead of 
`type`.
+  * `srclayers`: names of the source layers.
+  In SINGA, all connections are [converted](neural-net.html) to directed 
connections.
+  * `param`: configuration for a [Param](param.html) instance.
+  There can be multiple Param objects in one layer.
+
+Different layers may have different configurations. These configurations
+are defined in `<type>_conf`.  E.g., "fc1" layer has
+`innerproduct_conf`. The subsequent sections
+explain the functionality of each built-in layer and how to configure it.
+
+### Built-in Layer subclasses
+SINGA has provided many built-in layers, which can be used directly to create 
neural nets.
+These layers are categorized according to their functionalities,
+
+  * Input layers for loading records (e.g., images) from disk files, HDFS or 
network into memory.
+  * Neuron layers for feature transformation, e.g., 
[convolution](../api/classsinga_1_1ConvolutionLayer.html), 
[pooling](../api/classsinga_1_1PoolingLayer.html), dropout, etc.
+  * Loss layers for measuring the training objective loss, e.g., Cross Entropy 
loss or Euclidean loss.
+  * Output layers for outputting the prediction results (e.g., probabilities 
of each category) or features into persistent storage, e.g., disk or HDFS.
+  * Connection layers for connecting layers when the neural net is partitioned.
+
+#### Input layers
+
+Input layers load training/test data from disk or other places (e.g., HDFS or 
network)
+into memory.
+
+##### StoreInputLayer
+
+[StoreInputLayer](../api/classsinga_1_1StoreInputLayer.html) is a base layer 
for
+loading data from data store. The data store can be a KVFile or TextFile (LMDB,
+LevelDB, HDFS, etc., will be supported later). Its `ComputeFeature` function 
reads
+batchsize (string:key, string:value) tuples. Each tuple is parsed by a `Parse` 
function
+implemented by its subclasses.
+
+The configuration for this layer is in `store_conf`,
+
+    store_conf {
+      backend: # "kvfile" or "textfile"
+      path: # path to the data store
+      batchsize : 32
+      prefetching: true #default value is false
+      ...
+    }
+
+##### SingleLabelRecordLayer
+
+It is a subclass of StoreInputLayer. It assumes the (key, value) tuple loaded
+from a data store contains a feature vector (and a label) for one data 
instance.
+All feature vectors are of the same fixed length. The shape of one instance
+is configured through the `shape` field, e.g., the following configuration
+specifies the shape for the CIFAR10 images.
+
+    store_conf {
+      shape: 3  #channels
+      shape: 32 #height
+      shape: 32 #width
+    }
+
+It may do some preprocessing like 
[standardization](http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing).
+The data for preprocessing is loaded by and parsed in a virtual function, 
which is implemented by
+its subclasses.
+
+##### RecordInputLayer
+
+It is a subclass of SingleLabelRecordLayer. It parses the value field from one
+tuple into a RecordProto, which is generated by Google Protobuf according
+to common.proto.  It can be used to store features for images (e.g., using the 
pixel field)
+or other objects (using the data field). The key field is not parsed.
+
+    type: kRecordInput
+    store_conf {
+      has_label: # default is true
+      ...
+    }
+
+##### CSVInputLayer
+
+It is a subclass of SingleLabelRecordLayer. The value field from one tuple is 
parsed
+as a CSV line (separated by comma). The first number would be parsed as a 
label if
+`has_label` is configured in `store_conf`. Otherwise, all numbers would be 
parsed
+into one row of the `data_` Blob.
+
+    type: kCSVInput
+    store_conf {
+      has_label: # default is true
+      ...
+    }
+
+##### ImagePreprocessLayer
+
+This layer does image preprocessing, e.g., cropping, mirroring and scaling, 
against
+the data Blob from its source layer. It deprecates the RGBImageLayer which
+works on the Record from ShardDataLayer. It still uses the same configuration 
as
+RGBImageLayer,
+
+    type: kImagePreprocess
+    rgbimage_conf {
+      scale: float
+      cropsize: int  # cropping each image to keep the central part with this 
size
+      mirror: bool  # mirror the image by set image[i,j]=image[i,len-j]
+      meanfile: "Image_Mean_File_Path"
+    }
+
+##### ShardDataLayer (Deprected)
+Deprected! Please use ProtoRecordInputLayer or CSVRecordInputLayer.
+
+[ShardDataLayer](../api/classsinga_1_1ShardDataLayer.html) is a subclass of 
DataLayer,
+which reads Records from disk file. The file should be created using
+[DataShard](../api/classsinga_1_1DataShard.html)
+class. With the data file prepared, users configure the layer as
+
+    type: kShardData
+    sharddata_conf {
+      path: "path to data shard folder"
+      batchsize: int
+      random_skip: int
+    }
+
+`batchsize` specifies the number of records to be trained for one mini-batch.
+The first `rand() % random_skip` `Record`s will be skipped at the first
+iteration. This is to enforce that different workers work on different Records.
+
+##### LMDBDataLayer (Deprected)
+Deprected! Please use ProtoRecordInputLayer or CSVRecordInputLayer.
+
+[LMDBDataLayer] is similar to ShardDataLayer, except that the Records are
+loaded from LMDB.
+
+    type: kLMDBData
+    lmdbdata_conf {
+      path: "path to LMDB folder"
+      batchsize: int
+      random_skip: int
+    }
+
+##### ParserLayer (Deprected)
+Deprected! Please use ProtoRecordInputLayer or CSVRecordInputLayer.
+
+It get a vector of Records from DataLayer and parse features into
+a Blob.
+
+    virtual void ParseRecords(Phase phase, const vector<Record>& records, 
Blob<float>* blob) = 0;
+
+
+##### LabelLayer (Deprected)
+Deprected! Please use ProtoRecordInputLayer or CSVRecordInputLayer.
+
+[LabelLayer](../api/classsinga_1_1LabelLayer.html) is a subclass of 
ParserLayer.
+It parses a single label from each Record. Consequently, it
+will put $b$ (mini-batch size) values into the Blob. It has no specific 
configuration fields.
+
+
+##### MnistImageLayer (Deprected)
+Deprected! Please use ProtoRecordInputLayer or CSVRecordInputLayer.
+[MnistImageLayer] is a subclass of ParserLayer. It parses the pixel values of
+each image from the MNIST dataset. The pixel
+values may be normalized as `x/norm_a - norm_b`. For example, if `norm_a` is
+set to 255 and `norm_b` is set to 0, then every pixel will be normalized into
+[0, 1].
+
+    type: kMnistImage
+    mnistimage_conf {
+      norm_a: float
+      norm_b: float
+    }
+
+##### RGBImageLayer (Deprected)
+Deprected! Please use the ImagePreprocessLayer.
+[RGBImageLayer](../api/classsinga_1_1RGBImageLayer.html) is a subclass of 
ParserLayer.
+It parses the RGB values of one image from each Record. It may also
+apply some transformations, e.g., cropping, mirroring operations. If the
+`meanfile` is specified, it should point to a path that contains one Record for
+the mean of each pixel over all training images.
+
+    type: kRGBImage
+    rgbimage_conf {
+      scale: float
+      cropsize: int  # cropping each image to keep the central part with this 
size
+      mirror: bool  # mirror the image by set image[i,j]=image[i,len-j]
+      meanfile: "Image_Mean_File_Path"
+    }
+
+##### PrefetchLayer
+
+[PrefetchLayer](../api/classsinga_1_1PrefetchLayer.html) embeds other input 
layers
+to do data prefeching.  It will launch a thread to call the embedded layers to 
load and extract features.
+It ensures that the I/O task and computation task can work simultaneously.
+One example PrefetchLayer configuration is,
+
+    layer {
+      name: "prefetch"
+      type: kPrefetch
+      sublayers {
+        name: "data"
+        type: kShardData
+        sharddata_conf { }
+      }
+      sublayers {
+        name: "rgb"
+        type: kRGBImage
+        srclayers:"data"
+        rgbimage_conf { }
+      }
+      sublayers {
+        name: "label"
+        type: kLabel
+        srclayers: "data"
+      }
+      exclude:kTest
+    }
+
+The layers on top of the PrefetchLayer should use the name of the embedded
+layers as their source layers. For example, the "rgb" and "label" should be
+configured to the `srclayers` of other layers.
+
+
+#### Output Layers
+
+Output layers get data from their source layers and write them to persistent 
storage,
+e.g., disk files or HDFS (to be supported).
+
+##### RecordOutputLayer
+
+This layer gets data (and label if it is available) from its source layer and 
converts it into records of type
+RecordProto. Records are written as (key = instance No., value = serialized 
record) tuples into Store, e.g., KVFile. The configuration of this layer
+should include the specifics of the Store backend via `store_conf`.
+
+    layer {
+      name: "output"
+      type: kRecordOutput
+      srclayers:
+      store_conf {
+        backend: "kvfile"
+        path:
+      }
+    }
+
+##### CSVOutputLayer
+This layer gets data (and label if it available) from its source layer and 
converts it into
+a string per instance with fields separated by commas (i.e., CSV format). The 
shape information
+is not kept in the string. All strings are written into
+Store, e.g., text file. The configuration of this layer should include the 
specifics of the Store backend via `store_conf`.
+
+    layer {
+      name: "output"
+      type: kCSVOutput
+      srclayers:
+      store_conf {
+        backend: "textfile"
+        path:
+      }
+    }
+
+#### Neuron Layers
+
+Neuron layers conduct feature transformations.
+
+#### ActivationLayer
+
+    type: kActivation
+    activation_conf {
+      type: {RELU, SIGMOID, TANH, STANH}
+    }
+
+##### ConvolutionLayer
+
+[ConvolutionLayer](../api/classsinga_1_1ConvolutionLayer.html) conducts 
convolution transformation.
+
+    type: kConvolution
+    convolution_conf {
+      num_filters: int
+      kernel: int
+      stride: int
+      pad: int
+    }
+    param { } # weight/filter matrix
+    param { } # bias vector
+
+The int value `num_filters` stands for the count of the applied filters; the 
int
+value `kernel` stands for the convolution kernel size (equal width and height);
+the int value `stride` stands for the distance between the successive filters;
+the int value `pad` pads each with a given int number of pixels border of
+zeros.
+
+##### InnerProductLayer
+
+[InnerProductLayer](../api/classsinga_1_1InnerProductLayer.html) is fully 
connected with its (single) source layer.
+Typically, it has two parameter fields, one for weight matrix, and the other
+for bias vector. It rotates the feature of the source layer (by multiplying 
with weight matrix) and
+shifts it (by adding the bias vector).
+
+    type: kInnerProduct
+    innerproduct_conf {
+      num_output: int
+    }
+    param { } # weight matrix
+    param { } # bias vector
+
+
+##### PoolingLayer
+
+[PoolingLayer](../api/classsinga_1_1PoolingLayer.html) is used to do a 
normalization (or averaging or sampling) of the
+feature vectors from the source layer.
+
+    type: kPooling
+    pooling_conf {
+      pool: AVE|MAX // Choose whether use the Average Pooling or Max Pooling
+      kernel: int   // size of the kernel filter
+      pad: int      // the padding size
+      stride: int   // the step length of the filter
+    }
+
+The pooling layer has two methods: Average Pooling and Max Pooling.
+Use the enum AVE and MAX to choose the method.
+
+  * Max Pooling selects the max value for each filtering area as a point of the
+  result feature blob.
+  * Average Pooling averages all values for each filtering area at a point of 
the
+    result feature blob.
+
+##### ReLULayer
+
+[ReLuLayer](../api/classsinga_1_1ReLULayer.html) has rectified linear neurons, 
which conducts the following
+transformation, `f(x) = Max(0, x)`. It has no specific configuration fields.
+
+##### STanhLayer
+
+[STanhLayer](../api/classsinga_1_1TanhLayer.html) uses the scaled tanh as 
activation function, i.e., `f(x)=1.7159047* tanh(0.6666667 * x)`.
+It has no specific configuration fields.
+
+##### SigmoidLayer
+
+[SigmoidLayer] uses the sigmoid (or logistic) as activation function, i.e.,
+`f(x)=sigmoid(x)`.  It has no specific configuration fields.
+
+
+##### Dropout Layer
+[DropoutLayer](../api/asssinga_1_1DropoutLayer.html) is a layer that randomly 
dropouts some inputs.
+This scheme helps deep learning model away from over-fitting.
+
+    type: kDropout
+    dropout_conf {
+      dropout_ratio: float # dropout probability
+    }
+
+##### LRNLayer
+[LRNLayer](../api/classsinga_1_1LRNLayer.html), (Local Response 
Normalization), normalizes over the channels.
+
+    type: kLRN
+    lrn_conf {
+      local_size: int
+      alpha: float  // scaling parameter
+      beta: float   // exponential number
+    }
+
+`local_size` specifies  the quantity of the adjoining channels which will be 
summed up.
+ For `WITHIN_CHANNEL`, it means the side length of the space region which will 
be summed up.
+
+
+
+### CuDNN layers
+
+CuDNN v3 and v4 are supported in SINGA, which include the following layers,
+
+* CudnnActivationLayer (activation functions are SIGMOID, TANH, RELU)
+* CudnnConvLayer
+* CudnnLRNLayer
+* CudnnPoolLayer
+* CudnnSoftmaxLayer
+
+These layers have the same configuration as the corresponding CPU layers.
+For CuDNN v4, the batch normalization layer is added, which is named as
+`CudnnBMLayer`.
+
+
+#### Loss Layers
+
+Loss layers measures the objective training loss.
+
+##### SoftmaxLossLayer
+
+[SoftmaxLossLayer](../api/classsinga_1_1SoftmaxLossLayer.html) is a 
combination of the Softmax transformation and
+Cross-Entropy loss. It applies Softmax firstly to get a prediction probability
+for each output unit (neuron) and compute the cross-entropy against the ground 
truth.
+It is generally used as the final layer to generate labels for classification 
tasks.
+
+    type: kSoftmaxLoss
+    softmaxloss_conf {
+      topk: int
+    }
+
+The configuration field `topk` is for selecting the labels with `topk`
+probabilities as the prediction results. It is tedious for users to view the
+prediction probability of every label.
+
+#### ConnectionLayer
+
+Subclasses of ConnectionLayer are utility layers that connects other layers due
+to neural net partitioning or other cases.
+
+##### ConcateLayer
+
+[ConcateLayer](../api/classsinga_1_1ConcateLayer.html) connects more than one 
source layers to concatenate their feature
+blob along given dimension.
+
+    type: kConcate
+    concate_conf {
+      concate_dim: int  // define the dimension
+    }
+
+##### SliceLayer
+
+[SliceLayer](../api/classsinga_1_1SliceLayer.html) connects to more than one 
destination layers to slice its feature
+blob along given dimension.
+
+    type: kSlice
+    slice_conf {
+      slice_dim: int
+    }
+
+##### SplitLayer
+
+[SplitLayer](../api/classsinga_1_1SplitLayer.html) connects to more than one 
destination layers to replicate its
+feature blob.
+
+    type: kSplit
+    split_conf {
+      num_splits: int
+    }
+
+##### BridgeSrcLayer & BridgeDstLayer
+
+[BridgeSrcLayer](../api/classsinga_1_1BridgeSrcLayer.html) &
+[BridgeDstLayer](../api/classsinga_1_1BridgeDstLayer.html) are utility layers 
assisting data (e.g., feature or
+gradient) transferring due to neural net partitioning. These two layers are
+added implicitly. Users typically do not need to configure them in their neural
+net configuration.
+
+### OutputLayer
+
+It write the prediction results or the extracted features into file, HTTP 
stream
+or other places. Currently SINGA has not implemented any specific output layer.
+
+## Advanced user guide
+
+The base Layer class is introduced in this section, followed by how to
+implement a new Layer subclass.
+
+### Base Layer class
+
+#### Members
+
+    LayerProto layer_conf_;
+    vector<Blob<float>> datavec_, gradvec_;
+    vector<AuxType> aux_data_;
+
+The base layer class keeps the user configuration in `layer_conf_`.
+`datavec_` stores the features associated with this layer.
+There are layers without feature vectors; instead, they share the data from
+source layers.
+The `gradvec_` is for storing the gradients of the
+objective loss w.r.t. the `datavec_`. The `aux_data_` stores the auxiliary 
data, e.g., image label (set `AuxType` to int).
+If images have variant number of labels, the AuxType can be defined to 
`vector<int>`.
+Currently, we hard code `AuxType` to int. It will be added as a template 
argument of Layer class later.
+
+If a layer has parameters, these parameters are declared using type
+[Param](param.html). Since some layers do not have
+parameters, we do not declare any `Param` in the base layer class.
+
+#### Functions
+
+    virtual void Setup(const LayerProto& conf, const vector<Layer*>& 
srclayers);
+    virtual void ComputeFeature(int flag, const vector<Layer*>& srclayers) = 0;
+    virtual void ComputeGradient(int flag, const vector<Layer*>& srclayers) = 
0;
+
+The `Setup` function reads user configuration, i.e. `conf`, and information
+from source layers, e.g., mini-batch size,  to set the
+shape of the `data_` (and `grad_`) field as well
+as some other layer specific fields.
+Memory will not be allocated until computation over the data structure happens.
+
+The `ComputeFeature` function evaluates the feature blob by transforming (e.g.
+convolution and pooling) features from the source layers.  `ComputeGradient`
+computes the gradients of parameters associated with this layer.  These two
+functions are invoked by the [TrainOneBatch](train-one-batch.html)
+function during training. Hence, they should be consistent with the
+`TrainOneBatch` function. Particularly, for feed-forward and RNN models, they 
are
+trained using [BP algorithm](train-one-batch.html#back-propagation),
+which requires each layer's `ComputeFeature`
+function to compute `data_` based on source layers, and requires each layer's
+`ComputeGradient` to compute gradients of parameters and source layers'
+`grad_`. For energy models, e.g., RBM, they are trained by
+[CD algorithm](train-one-batch.html#contrastive-divergence), which
+requires each layer's `ComputeFeature` function to compute the feature vectors
+for the positive phase or negative phase depending on the `phase` argument, and
+requires the `ComputeGradient` function to only compute parameter gradients.
+For some layers, e.g., loss layer or output layer, they can put the loss or
+prediction result into the `metric` argument, which will be averaged and
+displayed periodically.
+
+### Implementing a new Layer subclass
+
+Users can extend the Layer class or other subclasses to implement their own 
feature transformation
+logics as long as the two virtual functions are overridden to be consistent 
with
+the `TrainOneBatch` function. The `Setup` function may also be overridden to
+read specific layer configuration.
+
+The [RNNLM](rnn.html) provides a couple of user-defined layers. You can refer 
to them as examples.
+
+#### Layer specific protocol message
+
+To implement a new layer, the first step is to define the layer specific
+configuration. Suppose the new layer is `FooLayer`, the layer specific
+google protocol message `FooLayerProto` should be defined as
+
+    # in user.proto
+    package singa
+    import "job.proto"
+    message FooLayerProto {
+      optional int32 a = 1;  // specific fields to the FooLayer
+    }
+
+In addition, users need to extend the original `LayerProto` (defined in 
job.proto of SINGA)
+to include the `foo_conf` as follows.
+
+    extend LayerProto {
+      optional FooLayerProto foo_conf = 101;  // unique field id, reserved for 
extensions
+    }
+
+If there are multiple new layers, then each layer that has specific
+configurations would have a `<type>_conf` field and takes one unique extension 
number.
+SINGA has reserved enough extension numbers, e.g., starting from 101 to 1000.
+
+    # job.proto of SINGA
+    LayerProto {
+      ...
+      extensions 101 to 1000;
+    }
+
+With user.proto defined, users can use
+[protoc](https://developers.google.com/protocol-buffers/) to generate the 
`user.pb.cc`
+and `user.pb.h` files.  In users' code, the extension fields can be accessed 
via,
+
+    auto conf = layer_proto_.GetExtension(foo_conf);
+    int a = conf.a();
+
+When defining configurations of the new layer (in job.conf), users should use
+`user_type` for its layer type instead of `type`. In addition, `foo_conf`
+should be enclosed in brackets.
+
+    layer {
+      name: "foo"
+      user_type: "kFooLayer"  # Note user_type of user-defined layers is string
+      [foo_conf] {      # Note there is a pair of [] for extension fields
+        a: 10
+      }
+    }
+
+#### New Layer subclass declaration
+
+The new layer subclass can be implemented like the built-in layer subclasses.
+
+    class FooLayer : public singa::Layer {
+     public:
+      void Setup(const LayerProto& conf, const vector<Layer*>& srclayers) 
override;
+      void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
+      void ComputeGradient(int flag, const vector<Layer*>& srclayers) override;
+
+     private:
+      //  members
+    };
+
+Users must override the two virtual functions to be called by the
+`TrainOneBatch` for either BP or CD algorithm. Typically, the `Setup` function
+will also be overridden to initialize some members. The user configured fields
+can be accessed through `layer_conf_` as shown in the above paragraphs.
+
+#### New Layer subclass registration
+
+The newly defined layer should be registered in 
[main.cc](http://singa.incubator.apache.org/docs/programming-guide) by adding
+
+    driver.RegisterLayer<FooLayer, std::string>("kFooLayer"); // "kFooLayer" 
should be matched to layer configurations in job.conf.
+
+After that, the [NeuralNet](neural-net.html) can create instances of the new 
Layer subclass.

Added: incubator/singa/site/trunk/content/markdown/v0.3.0/mesos.md
URL: 
http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/v0.3.0/mesos.md?rev=1740048&view=auto
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--- incubator/singa/site/trunk/content/markdown/v0.3.0/mesos.md (added)
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+#Distributed Training on Mesos
+
+This guide explains how to start SINGA distributed training on a Mesos 
cluster. It assumes that both Mesos and HDFS are already running, and every 
node has SINGA installed.
+We assume the architecture depicted below, in which a cluster nodes are Docker 
container. Refer to [Docker guide](docker.html) for details of how to start 
individual nodes and set up network connection between them (make sure 
[weave](http://weave.works/guides/weave-docker-ubuntu-simple.html) is running 
at each node, and the cluster's headnode is running in container `node0`)
+
+![Nothing](http://www.comp.nus.edu.sg/~dinhtta/files/singa_mesos.png)
+
+---
+
+## Start HDFS and Mesos
+Go inside each container, using:
+````
+docker exec -it nodeX /bin/bash
+````
+and configure it as follows:
+
+* On container `node0`
+
+        hadoop namenode -format
+        hadoop-daemon.sh start namenode
+        /opt/mesos-0.22.0/build/bin/mesos-master.sh --work_dir=/opt 
--log_dir=/opt --quiet > /dev/null &
+        zk-service.sh start
+
+* On container `node1, node2, ...`
+
+        hadoop-daemon.sh start datanode
+        /opt/mesos-0.22.0/build/bin/mesos-slave.sh --master=node0:5050 
--log_dir=/opt --quiet > /dev/null &
+
+To check if the setup has been successful, check that HDFS namenode has 
registered `N` datanodes, via:
+
+````
+hadoop dfsadmin -report
+````
+
+####Important If the Docker version is 1.9 or newer, make sure [name 
resolution is set up
+properly](docker.html#launch_pseudo) 
+
+#### Mesos logs
+Mesos logs are stored at `/opt/lt-mesos-master.INFO` on `node0` and 
`/opt/lt-mesos-slave.INFO` at other nodes.
+
+---
+
+## Starting SINGA training on Mesos
+Assumed that Mesos and HDFS are already started, SINGA job can be launched at 
**any** container.
+
+#### Launching job
+
+1. Log in to any container, then
+        cd incubator-singa/tool/mesos
+<a name="job_start"></a>
+2. Check that configuration files are correct:
+    + `scheduler.conf` contains information about the master nodes
+    + `singa.conf` contains information about Zookeeper node0
+    + Job configuration file `job.conf` **contains full path to the examples 
directories (NO RELATIVE PATH!).**
+3. Start the job:
+    + If starting for the first time:
+
+                 ./scheduler <job config file> -scheduler_conf <scheduler 
config file> -singa_conf <SINGA config file>
+    + If not the first time:
+
+                 ./scheduler <job config file>
+
+**Notes.** Each running job is given a `frameworkID`. Look for the log message 
of the form:
+
+             Framework registered with XXX-XXX-XXX-XXX-XXX-XXX
+
+#### Monitoring and Debugging
+
+Each Mesos job is given a `frameworkID` and a *sandbox* directory is created 
for each job.
+The directory is in the specified `work_dir` (or `/tmp/mesos`) by default. For 
example, the error
+during SINGA execution can be found at:
+
+            
/tmp/mesos/slaves/xxxxx-Sx/frameworks/xxxxx/executors/SINGA_x/runs/latest/stderr
+
+Other artifacts, like files downloaded from HDFS (`job.conf`) and `stdout` can 
be found in the same
+directory.
+
+#### Stopping
+
+There are two way to kill the running job:
+
+1. If the scheduler is running in the foreground, simply kill it (using 
`Ctrl-C`, for example).
+
+2. If the scheduler is running in the background, kill it using Mesos's REST 
API:
+
+          curl -d "frameworkId=XXX-XXX-XXX-XXX-XXX-XXX" -X POST 
http://<master>/master/shutdown
+