You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@singa.apache.org by wa...@apache.org on 2016/01/13 04:46:20 UTC
svn commit: r1724348 [6/6] - in
/incubator/singa/site/trunk/content/markdown/docs: ./ jp/ kr/
Added: incubator/singa/site/trunk/content/markdown/docs/kr/rnn.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/kr/rnn.md?rev=1724348&view=auto
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/kr/rnn.md (added)
+++ incubator/singa/site/trunk/content/markdown/docs/kr/rnn.md Wed Jan 13 03:46:19 2016
@@ -0,0 +1,420 @@
+# 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.
Added: incubator/singa/site/trunk/content/markdown/docs/kr/test.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/kr/test.md?rev=1724348&view=auto
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/kr/test.md (added)
+++ incubator/singa/site/trunk/content/markdown/docs/kr/test.md Wed Jan 13 03:46:19 2016
@@ -0,0 +1,119 @@
+# 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.
Added: incubator/singa/site/trunk/content/markdown/docs/kr/train-one-batch.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/kr/train-one-batch.md?rev=1724348&view=auto
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/kr/train-one-batch.md (added)
+++ incubator/singa/site/trunk/content/markdown/docs/kr/train-one-batch.md Wed Jan 13 03:46:19 2016
@@ -0,0 +1,179 @@
+# 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;
+ }
Added: incubator/singa/site/trunk/content/markdown/docs/kr/updater.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/kr/updater.md?rev=1724348&view=auto
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/kr/updater.md (added)
+++ incubator/singa/site/trunk/content/markdown/docs/kr/updater.md Wed Jan 13 03:46:19 2016
@@ -0,0 +1,284 @@
+# 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")