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refine docs
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fitzwang committed Jan 11, 2019
1 parent f01a4fb commit c472054
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Showing 19 changed files with 142 additions and 598 deletions.
26 changes: 13 additions & 13 deletions angel-ps/mllib/src/main/java/com/tencent/angel/ml/psf/optimizer/AdaDeltaUpdateFunc.java
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Original file line number Diff line number Diff line change
Expand Up @@ -16,23 +16,24 @@ public AdaDeltaUpdateFunc() {
super();
}

public AdaDeltaUpdateFunc(int matId, int factor, double epsilon, double beta, double lr, double regL1Param, double regL2Param, int epoch) {
super(matId, new int[]{factor}, new double[]{epsilon, beta, lr, regL1Param, regL2Param, epoch, 1});
public AdaDeltaUpdateFunc(int matId, int factor, double epsilon, double alpha, double beta, double lr, double regL1Param, double regL2Param, int epoch) {
super(matId, new int[]{factor}, new double[]{epsilon, alpha, beta, lr, regL1Param, regL2Param, epoch, 1});
}

public AdaDeltaUpdateFunc(int matId, int factor, double epsilon, double beta, double lr, double regL1Param, double regL2Param, int epoch, int batchSize) {
super(matId, new int[]{factor}, new double[]{epsilon, beta, lr, regL1Param, regL2Param, epoch, batchSize});
public AdaDeltaUpdateFunc(int matId, int factor, double epsilon, double alpha, double beta, double lr, double regL1Param, double regL2Param, int epoch, int batchSize) {
super(matId, new int[]{factor}, new double[]{epsilon, alpha, beta, lr, regL1Param, regL2Param, epoch, batchSize});
}

@Override
public void update(ServerPartition partition, int factor, double[] scalars) {
double epsilon = scalars[0];
double beta = scalars[1];
double lr = scalars[2];
double l1RegParam = scalars[3];
double l2RegParam = scalars[4];
double epoch = (int) scalars[5];
double batchSize = (int) scalars[6];
double alpha = scalars[1];
double beta = scalars[2];
double lr = scalars[3];
double l1RegParam = scalars[4];
double l2RegParam = scalars[5];
double epoch = (int) scalars[6];
double batchSize = (int) scalars[7];

for (int f = 0; f < factor; f++) {
ServerRow gradientServerRow = partition.getRow(f + 3 * factor);
Expand All @@ -47,7 +48,7 @@ public void update(ServerPartition partition, int factor, double[] scalars) {
gradient.idiv(batchSize);
}

OptFuncs.iexpsmoothing2(square1, gradient, beta);
OptFuncs.iexpsmoothing2(square1, gradient, alpha);
Vector hessian = OptFuncs.adadeltahessian(square1, square2);

if (l2RegParam != 0) {
Expand All @@ -56,13 +57,12 @@ public void update(ServerPartition partition, int factor, double[] scalars) {

OptFuncs.iadadeltadelta(gradient, hessian, l2RegParam);
weight.isub(gradient);
OptFuncs.iexpsmoothing2(square2, gradient, beta);

if (l1RegParam != 0) {
OptFuncs.iadadeltathredshold(weight, hessian, l1RegParam, l2RegParam);
}

OptFuncs.iexpsmoothing2(square2, gradient, beta);

gradient.clear();
} finally {
gradientServerRow.endWrite();
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20 changes: 8 additions & 12 deletions angel-ps/mllib/src/main/scala/com/tencent/angel/ml/core/conf/MLConf.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,14 @@ object MLConf {
// Momentum
val ML_OPT_MOMENTUM_MOMENTUM = "ml.opt.momentum.momentum"
val DEFAULT_ML_OPT_MOMENTUM_MOMENTUM = 0.9
// AdaDelta
val ML_OPT_ADADELTA_ALPHA = "ml.opt.adadelta.alpha"
val DEFAULT_ML_OPT_ADADELTA_ALPHA = 0.9
val ML_OPT_ADADELTA_BETA = "ml.opt.adadelta.beta"
val DEFAULT_ML_OPT_ADADELTA_BETA = 0.9
// AdaGrad
val ML_OPT_ADAGRAD_BETA = "ml.opt.adadelta.beta"
val DEFAULT_ML_OPT_ADADGRAD_BETA = 0.9
// Adam
val ML_OPT_ADAM_GAMMA = "ml.opt.adam.gamma"
val DEFAULT_ML_OPT_ADAM_GAMMA = 0.99
Expand All @@ -115,12 +123,6 @@ object MLConf {
val DEFAULT_ML_OPT_FTRL_ALPHA = 0.1
val ML_OPT_FTRL_BETA = "ml.opt.ftrl.beta"
val DEFAULT_ML_OPT_FTRL_BETA = 1.0
// AdaDelta
val ML_OPT_ADADELTA_BETA = "ml.opt.adadelta.beta"
val DEFAULT_ML_OPT_ADADELTA_BETA = 0.9
// AdaGrad
val ML_OPT_ADAGRAD_BETA = "ml.opt.adadelta.beta"
val DEFAULT_ML_OPT_ADADGRAD_BETA = 0.9

// Decays
val ML_OPT_DECAY_CLASS_NAME = "ml.opt.decay.class.name"
Expand All @@ -147,12 +149,6 @@ object MLConf {
val DEFAULT_ML_RANK_NUM = 8

// (MLP) Layer params
val ML_MLP_INPUT_LAYER_PARAMS = "ml.mlp.input.layer.params"
val DEFAULT_ML_MLP_INPUT_LAYER_PARAMS = "100,identity"
val ML_MLP_HIDEN_LAYER_PARAMS = "ml.mlp.hidden.layer.params"
val DEFAULT_ML_MLP_HIDEN_LAYER_PARAMS = "100,relu|100,relu|1,identity"
val ML_MLP_LOSS_LAYER_PARAMS = "ml.mlp.loss.layer.params"
val DEFAULT_ML_MLP_LOSS_LAYER_PARAMS = "logloss"
val ML_NUM_CLASS = "ml.num.class"
val DEFAULT_ML_NUM_CLASS = 2

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8 changes: 4 additions & 4 deletions angel-ps/mllib/src/main/scala/com/tencent/angel/ml/core/optimizer/AdaDelta.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -7,22 +7,22 @@ import com.tencent.angel.ml.psf.optimizer.AdaDeltaUpdateFunc
import com.tencent.angel.psagent.PSAgentContext
import org.apache.commons.logging.LogFactory

class AdaDelta(stepSize: Double, val beta: Double = 0.9) extends Optimizer(stepSize) {
class AdaDelta(stepSize: Double, val alpha: Double, val beta: Double = 0.9) extends Optimizer(stepSize) {
private val LOG = LogFactory.getLog(classOf[AdaDelta])
override protected var numSlot: Int = 3

override def update(matrixId: Int, numFactors: Int, epoch: Int): Future[VoidResult] = {

val func = new AdaDeltaUpdateFunc(matrixId, numFactors, epsilon, beta, lr, regL1Param, regL2Param, epoch)
val func = new AdaDeltaUpdateFunc(matrixId, numFactors, epsilon, alpha, beta, lr, regL1Param, regL2Param, epoch)
PSAgentContext.get().getUserRequestAdapter.update(func)
}

override def update(matrixId: Int, numFactors: Int, epoch: Int, batchSize: Int): Future[VoidResult] = {
val func = new AdaDeltaUpdateFunc(matrixId, numFactors, epsilon, beta, lr, regL1Param, regL2Param, epoch, batchSize)
val func = new AdaDeltaUpdateFunc(matrixId, numFactors, epsilon, alpha, beta, lr, regL1Param, regL2Param, epoch, batchSize)
PSAgentContext.get().getUserRequestAdapter.update(func)
}

override def toString: String = {
s"AdaDelta beta=$beta lr=$lr regL2=$regL2Param regL1=$regL1Param epsilon=$epsilon"
s"AdaDelta alpha=$alpha beta=$beta lr=$lr regL2=$regL2Param regL1=$regL1Param epsilon=$epsilon"
}
}
10 changes: 8 additions & 2 deletions angel-ps/mllib/src/main/scala/com/tencent/angel/ml/core/utils/paramsutils/OptParams.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ object OptParams {
case jast: JString if nameMatch(jast, adagrad) =>
AdaGradParams(jast.extract[String].trim, Some(1.0), Some(0.9), None, None)
case jast: JString if nameMatch(jast, adadelta) =>
AdaDeltaParams(jast.extract[String].trim, Some(1.0), Some(0.9), None, None)
AdaDeltaParams(jast.extract[String].trim, Some(1.0), Some(0.9), Some(0.9), None, None)
case jast: JString if nameMatch(jast, sgd) =>
new OptParams(jast.extract[String].trim, Some(1.0), None, None)
case jast: JString => throw new AngelException(s"No such a optimizer: ${jast.extract[String]}!")
Expand Down Expand Up @@ -123,12 +123,17 @@ object OptParams {

AdaGradParams(optType, learningRate, beta, reg1, reg2)
case `adadelta` =>
val alpha: Option[Double] = json \ ParamKeys.alpha match {
case JNothing => Some(0.9)
case v: JValue => Some(v.extract[Double])
}

val beta: Option[Double] = json \ ParamKeys.beta match {
case JNothing => Some(0.9)
case v: JValue => Some(v.extract[Double])
}

AdaDeltaParams(optType, learningRate, beta, reg1, reg2)
AdaDeltaParams(optType, learningRate, alpha, beta, reg1, reg2)
case `sgd` =>
new OptParams(optType, learningRate, reg1, reg2)
case _ => throw new AngelException(s"No such a optimizer: $optType!")
Expand Down Expand Up @@ -193,6 +198,7 @@ case class AdaGradParams(override val name: String,

case class AdaDeltaParams(override val name: String,
override val lr: Option[Double],
alpha: Option[Double],
beta: Option[Double],
override val reg1: Option[Double],
override val reg2: Option[Double]) extends OptParams(name, lr, reg1, reg2) {
Expand Down
149 changes: 0 additions & 149 deletions docs/algo/admm_lr_on_angel.md
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