org.tensorflow.op.core

Class SdcaOptimizer

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.SdcaOptimizer

All Implemented Interfaces:

Op

@Operator
public final class SdcaOptimizer
extends PrimitiveOp

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012

$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$

[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SdcaOptimizer.Options Optional attributes for SdcaOptimizer

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
static SdcaOptimizer.Options	adaptative(Boolean adaptative)
static SdcaOptimizer	create(Scope scope, Iterable<Operand<Long>> sparseExampleIndices, Iterable<Operand<Long>> sparseFeatureIndices, Iterable<Operand<Float>> sparseFeatureValues, Iterable<Operand<Float>> denseFeatures, Operand<Float> exampleWeights, Operand<Float> exampleLabels, Iterable<Operand<Long>> sparseIndices, Iterable<Operand<Float>> sparseWeights, Iterable<Operand<Float>> denseWeights, Operand<Float> exampleStateData, String lossType, Float l1, Float l2, Long numLossPartitions, Long numInnerIterations, SdcaOptimizer.Options... options) Factory method to create a class to wrap a new SdcaOptimizer operation to the graph.
List<Output<Float>>	outDeltaDenseWeights() a list of vectors where the values are the delta weights associated with a dense feature group.
List<Output<Float>>	outDeltaSparseWeights() a list of vectors where each value is the delta weights associated with a sparse feature group.
Output<Float>	outExampleStateData() a list of vectors containing the updated example state data.

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

create

public static SdcaOptimizer create(Scope scope,
                                   Iterable<Operand<Long>> sparseExampleIndices,
                                   Iterable<Operand<Long>> sparseFeatureIndices,
                                   Iterable<Operand<Float>> sparseFeatureValues,
                                   Iterable<Operand<Float>> denseFeatures,
                                   Operand<Float> exampleWeights,
                                   Operand<Float> exampleLabels,
                                   Iterable<Operand<Long>> sparseIndices,
                                   Iterable<Operand<Float>> sparseWeights,
                                   Iterable<Operand<Float>> denseWeights,
                                   Operand<Float> exampleStateData,
                                   String lossType,
                                   Float l1,
                                   Float l2,
                                   Long numLossPartitions,
                                   Long numInnerIterations,
                                   SdcaOptimizer.Options... options)

Factory method to create a class to wrap a new SdcaOptimizer operation to the graph.

Parameters:

scope - current graph scope

sparseExampleIndices - a list of vectors which contain example indices.

sparseFeatureIndices - a list of vectors which contain feature indices.

sparseFeatureValues - a list of vectors which contains feature value associated with each feature group.

denseFeatures - a list of matrices which contains the dense feature values.

exampleWeights - a vector which contains the weight associated with each example.

exampleLabels - a vector which contains the label/target associated with each example.

sparseIndices - a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.

sparseWeights - a list of vectors where each value is the weight associated with a sparse feature group.

denseWeights - a list of vectors where the values are the weights associated with a dense feature group.

exampleStateData - a list of vectors containing the example state data.

lossType - Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.

l1 - Symmetric l1 regularization strength.

l2 - Symmetric l2 regularization strength.

numLossPartitions - Number of partitions of the global loss function.

numInnerIterations - Number of iterations per mini-batch.

options - carries optional attributes values

Returns:

a new instance of SdcaOptimizer

adaptative

public static SdcaOptimizer.Options adaptative(Boolean adaptative)

Parameters:

adaptative - Whether to use Adaptive SDCA for the inner loop.

outExampleStateData

public Output<Float> outExampleStateData()

a list of vectors containing the updated example state data.

outDeltaSparseWeights

public List<Output<Float>> outDeltaSparseWeights()

a list of vectors where each value is the delta weights associated with a sparse feature group.

outDeltaDenseWeights

public List<Output<Float>> outDeltaDenseWeights()

a list of vectors where the values are the delta weights associated with a dense feature group.