Packages

class YellowFin extends GradientDescent

Linear Supertypes
GradientDescent, Optimizer, AnyRef, Any
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Inherited
  1. YellowFin
  2. GradientDescent
  3. Optimizer
  4. AnyRef
  5. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new YellowFin(learningRate: Float = 1.0f, decay: Schedule[Float] = FixedSchedule[Float](), momentum: Float = 0.0f, beta: Float = 0.999f, curvatureWindowWidth: Int = 20, zeroDebias: Boolean = true, sparsityDebias: Boolean = true, useNesterov: Boolean = false, useLocking: Boolean = false, learningRateSummaryTag: String = null, name: String = "YellowFin")
    Attributes
    protected

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def applyDense[T, I](gradient: Output[T], variable: variables.Variable[T], iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): UntypedOp
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  5. def applyGradients[T, I](gradientsAndVariables: Seq[(OutputLike[T], variables.Variable[Any])], iteration: Option[variables.Variable[I]] = None, name: String = this.name)(implicit arg0: core.types.TF[T], arg1: LongDefault[I], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): UntypedOp
    Definition Classes
    YellowFin → Optimizer
  6. def applySparse[T, I](gradient: OutputIndexedSlices[T], variable: variables.Variable[T], iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): UntypedOp
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  7. def applySparseDuplicateIndices[T, I](gradient: OutputIndexedSlices[T], variable: variables.Variable[T], iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): UntypedOp
    Definition Classes
    GradientDescent → Optimizer
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. val beta: Float
  10. var betaTensor: Output[Float]
    Attributes
    protected
  11. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  12. def computeGradients[T](loss: Output[T], lossGradients: Seq[OutputLike[T]] = null, variables: Set[variables.Variable[Any]] = null, gradientsGatingMethod: GatingMethod = Gradients.OpGating, gradientsAggregationMethod: AggregationMethod = Gradients.AddAggregationMethod, colocateGradientsWithOps: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Seq[(OutputLike[T], variables.Variable[Any])]
    Definition Classes
    Optimizer
    Annotations
    @throws( ... )
  13. def createSlots(variables: Seq[variables.Variable[Any]]): Unit
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  14. def curvatureRange(gradNormSquaredSum: Output[Float], sparsityAvg: Option[Output[Float]]): (Output[Float], Output[Float])
    Attributes
    protected
  15. var curvatureWindow: variables.Variable[Float]
    Attributes
    protected
  16. val curvatureWindowWidth: Int
  17. val decay: Schedule[Float]
    Definition Classes
    YellowFin → GradientDescent
  18. def distanceToOptimum(gradNormSquaredSum: Output[Float], gradNormSquaredAvg: Output[Float], sparsityAvg: Option[Output[Float]]): Output[Float]
    Attributes
    protected
  19. var doTune: Output[Boolean]
    Attributes
    protected
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def finish(updateOps: Set[UntypedOp], nameScope: String): UntypedOp
    Definition Classes
    Optimizer
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. def getLearningRate[V, I](variable: variables.Variable[V], iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[V], arg1: core.types.TF[I], arg2: core.types.IsIntOrLong[I]): Output[V]
    Attributes
    protected
    Definition Classes
    YellowFin → GradientDescent
  26. def getMomentum[V](variable: variables.Variable[V])(implicit arg0: core.types.TF[V]): Output[V]
    Attributes
    protected
    Definition Classes
    YellowFin → GradientDescent
  27. final def getNonSlotVariable[T](name: String, graph: core.Graph = null)(implicit arg0: core.types.TF[T]): variables.Variable[T]
    Attributes
    protected
    Definition Classes
    Optimizer
  28. final def getNonSlotVariables: Iterable[variables.Variable[Any]]
    Attributes
    protected
    Definition Classes
    Optimizer
  29. final def getOrCreateNonSlotVariable[T](name: String, initialValue: tensors.Tensor[T], colocationOps: Set[UntypedOp] = Set.empty, ignoreExisting: Boolean = false)(implicit arg0: core.types.TF[T]): variables.Variable[T]
    Attributes
    protected
    Definition Classes
    Optimizer
  30. final def getSlot[T, R](name: String, variable: variables.Variable[T])(implicit arg0: core.types.TF[T], arg1: core.types.TF[R]): variables.Variable[R]
    Attributes
    protected
    Definition Classes
    Optimizer
  31. final def getSlot[T, R](name: String, variable: variables.Variable[T], dataType: core.types.DataType[R], initializer: Initializer, shape: core.Shape, variableScope: String)(implicit arg0: core.types.TF[T], arg1: core.types.TF[R]): variables.Variable[R]
    Attributes
    protected
    Definition Classes
    Optimizer
  32. def gradientsSparsity(gradients: Seq[Output[Float]]): Option[Output[Float]]
    Attributes
    protected
  33. def gradientsVariance(gradients: Seq[OutputLike[Float]], gradNormSquaredAvg: Output[Float], sparsityAvg: Option[Output[Float]]): Output[Float]
    Attributes
    protected
  34. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  35. val ignoreDuplicateSparseIndices: Boolean
    Definition Classes
    GradientDescent → Optimizer
  36. var incrementStepOp: UntypedOp
    Attributes
    protected
  37. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  38. val learningRate: Float
    Definition Classes
    YellowFin → GradientDescent
  39. var learningRateFactorVariable: variables.Variable[Float]
    Attributes
    protected
  40. val learningRateSummaryTag: String
    Definition Classes
    YellowFin → GradientDescent
  41. var learningRateTensor: Output[Float]
    Attributes
    protected
    Definition Classes
    GradientDescent
  42. var learningRateVariable: variables.Variable[Float]
    Attributes
    protected
  43. def minimize[T, I](loss: Output[T], lossGradients: Seq[OutputLike[T]] = null, variables: Set[variables.Variable[Any]] = null, gradientsGatingMethod: GatingMethod = Gradients.OpGating, gradientsAggregationMethod: AggregationMethod = Gradients.AddAggregationMethod, colocateGradientsWithOps: Boolean = false, iteration: Option[variables.Variable[I]] = None, name: String = "Minimize")(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T], arg2: LongDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): UntypedOp
    Definition Classes
    Optimizer
    Annotations
    @throws( ... )
  44. val momentum: Float
    Definition Classes
    YellowFin → GradientDescent
  45. var momentumTensor: Output[Float]
    Attributes
    protected
    Definition Classes
    GradientDescent
  46. var momentumVariable: variables.Variable[Float]
    Attributes
    protected
  47. var movingAverage: ExponentialMovingAverage
    Attributes
    protected
  48. val name: String
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  49. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  50. final val nonSlotVariables: Map[(String, Option[core.Graph]), variables.Variable[Any]]
    Attributes
    protected
    Definition Classes
    Optimizer
  51. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  52. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  53. def prepare[I](iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[I], arg1: core.types.IsIntOrLong[I]): Unit
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  54. final def slotNames: Set[String]
    Attributes
    protected
    Definition Classes
    Optimizer
  55. final val slots: Map[String, Map[variables.Variable[Any], variables.Variable[Any]]]
    Attributes
    protected
    Definition Classes
    Optimizer
  56. val sparsityDebias: Boolean
  57. final def state: Seq[variables.Variable[Any]]
    Definition Classes
    Optimizer
  58. var step: variables.Variable[Int]
    Attributes
    protected
  59. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  60. def toString(): String
    Definition Classes
    AnyRef → Any
  61. val useLocking: Boolean
    Definition Classes
    YellowFin → GradientDescent → Optimizer
  62. val useNesterov: Boolean
    Definition Classes
    YellowFin → GradientDescent
  63. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  64. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  65. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  66. def yellowFinUpdate[T](gradientsAndVariables: Seq[(OutputLike[T], variables.Variable[Any])])(implicit arg0: core.types.TF[T]): UntypedOp
    Attributes
    protected
  67. val zeroDebias: Boolean
  68. final def zerosSlot[T](name: String, variable: variables.Variable[T], variableScope: String)(implicit arg0: core.types.TF[T]): variables.Variable[T]
    Attributes
    protected
    Definition Classes
    Optimizer

Inherited from GradientDescent

Inherited from Optimizer

Inherited from AnyRef

Inherited from Any

Ungrouped