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class AdaDelta extends Optimizer

Linear Supertypes
Optimizer, AnyRef, Any
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  1. AdaDelta
  2. Optimizer
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Instance Constructors

  1. new AdaDelta(learningRate: Float = 0.01f, decay: Schedule[Float] = FixedSchedule[Float](), rho: Float = 0.95f, epsilon: Float = 1e-8f, ignoreDuplicateSparseIndices: Boolean = false, useLocking: Boolean = false, learningRateSummaryTag: String = null, name: String = "AdaDelta")
    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
    AdaDelta → 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
    Optimizer
    Annotations
    @throws( ... )
  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
    AdaDelta → 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
    Optimizer
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  10. 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( ... )
  11. def createSlots(variables: Seq[variables.Variable[Any]]): Unit
    Definition Classes
    AdaDelta → Optimizer
  12. val decay: Schedule[Float]
  13. val epsilon: Float
  14. var epsilonTensor: Output[Float]
    Attributes
    protected
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def finish(updateOps: Set[UntypedOp], nameScope: String): UntypedOp
    Definition Classes
    Optimizer
  19. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. def getEpsilon[V](variable: variables.Variable[V])(implicit arg0: core.types.TF[V]): Output[V]
    Attributes
    protected
  21. 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
  22. final def getNonSlotVariable[T](name: String, graph: core.Graph = null)(implicit arg0: core.types.TF[T]): variables.Variable[T]
    Attributes
    protected
    Definition Classes
    Optimizer
  23. final def getNonSlotVariables: Iterable[variables.Variable[Any]]
    Attributes
    protected
    Definition Classes
    Optimizer
  24. 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
  25. def getRho[V](variable: variables.Variable[V])(implicit arg0: core.types.TF[V]): Output[V]
    Attributes
    protected
  26. 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
  27. 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
  28. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  29. val ignoreDuplicateSparseIndices: Boolean
    Definition Classes
    AdaDelta → Optimizer
  30. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  31. val learningRate: Float
  32. val learningRateSummaryTag: String
  33. var learningRateTensor: Output[Float]
    Attributes
    protected
  34. 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( ... )
  35. val name: String
    Definition Classes
    AdaDelta → Optimizer
  36. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  37. final val nonSlotVariables: Map[(String, Option[core.Graph]), variables.Variable[Any]]
    Attributes
    protected
    Definition Classes
    Optimizer
  38. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  39. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  40. def prepare[I](iteration: Option[variables.Variable[I]])(implicit arg0: core.types.TF[I], arg1: core.types.IsIntOrLong[I]): Unit
    Definition Classes
    AdaDelta → Optimizer
  41. val rho: Float
  42. var rhoTensor: Output[Float]
    Attributes
    protected
  43. final def slotNames: Set[String]
    Attributes
    protected
    Definition Classes
    Optimizer
  44. final val slots: Map[String, Map[variables.Variable[Any], variables.Variable[Any]]]
    Attributes
    protected
    Definition Classes
    Optimizer
  45. final def state: Seq[variables.Variable[Any]]
    Definition Classes
    Optimizer
  46. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  47. def toString(): String
    Definition Classes
    AnyRef → Any
  48. val useLocking: Boolean
    Definition Classes
    AdaDelta → Optimizer
  49. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  50. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  51. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  52. 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 Optimizer

Inherited from AnyRef

Inherited from Any

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