public class GradientDescent extends Object implements Optimizer, Logging
Modifier and Type | Method and Description |
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Vector |
optimize(RDD<scala.Tuple2<Object,Vector>> data,
Vector initialWeights)
:: DeveloperApi ::
Runs gradient descent on the given training data.
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static scala.Tuple2<Vector,double[]> |
runMiniBatchSGD(RDD<scala.Tuple2<Object,Vector>> data,
Gradient gradient,
Updater updater,
double stepSize,
int numIterations,
double regParam,
double miniBatchFraction,
Vector initialWeights)
Run stochastic gradient descent (SGD) in parallel using mini batches.
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GradientDescent |
setGradient(Gradient gradient)
Set the gradient function (of the loss function of one single data example)
to be used for SGD.
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GradientDescent |
setMiniBatchFraction(double fraction)
:: Experimental ::
Set fraction of data to be used for each SGD iteration.
|
GradientDescent |
setNumIterations(int iters)
Set the number of iterations for SGD.
|
GradientDescent |
setRegParam(double regParam)
Set the regularization parameter.
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GradientDescent |
setStepSize(double step)
Set the initial step size of SGD for the first step.
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GradientDescent |
setUpdater(Updater updater)
Set the updater function to actually perform a gradient step in a given direction.
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public static scala.Tuple2<Vector,double[]> runMiniBatchSGD(RDD<scala.Tuple2<Object,Vector>> data, Gradient gradient, Updater updater, double stepSize, int numIterations, double regParam, double miniBatchFraction, Vector initialWeights)
data
- - Input data for SGD. RDD of the set of data examples, each of
the form (label, [feature values]).gradient
- - Gradient object (used to compute the gradient of the loss function of
one single data example)updater
- - Updater function to actually perform a gradient step in a given direction.stepSize
- - initial step size for the first stepnumIterations
- - number of iterations that SGD should be run.regParam
- - regularization parameterminiBatchFraction
- - fraction of the input data set that should be used for
one iteration of SGD. Default value 1.0.
initialWeights
- (undocumented)public GradientDescent setStepSize(double step)
step
- (undocumented)public GradientDescent setMiniBatchFraction(double fraction)
fraction
- (undocumented)public GradientDescent setNumIterations(int iters)
iters
- (undocumented)public GradientDescent setRegParam(double regParam)
regParam
- (undocumented)public GradientDescent setGradient(Gradient gradient)
gradient
- (undocumented)public GradientDescent setUpdater(Updater updater)
updater
- (undocumented)