Class/Object

org.platanios.tensorflow.api.ops.training.optimizers

Adam

Related Docs: object Adam | package optimizers

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case class Adam(learningRate: Double = 0.001, decay: Decay = NoDecay, beta1: Double = 0.9, beta2: Double = 0.999, useNesterov: Boolean = false, epsilon: Double = 1e-8, useLocking: Boolean = false, learningRateSummaryTag: String = null, name: String = "Adam") extends Optimizer with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, Optimizer, AnyRef, Any
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Inherited
  1. Adam
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Optimizer
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new Adam(learningRate: Double = 0.001, decay: Decay = NoDecay, beta1: Double = 0.9, beta2: Double = 0.999, useNesterov: Boolean = false, epsilon: Double = 1e-8, useLocking: Boolean = false, learningRateSummaryTag: String = null, name: String = "Adam")

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  4. def applyDense(gradient: Output, variable: variables.Variable, iteration: Option[variables.Variable]): Op

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    Definition Classes
    Adam → Optimizer

  5. def applyGradients(gradientsAndVariables: Seq[(OutputLike, variables.Variable)], iteration: Option[variables.Variable] = None, name: String = this.name): Op

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    Definition Classes
    Optimizer

  6. def applySparse(gradient: OutputIndexedSlices, variable: variables.Variable, iteration: Option[variables.Variable]): Op

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    Definition Classes
    Adam → Optimizer

  7. def applySparseDuplicateIndices(gradient: OutputIndexedSlices, variable: variables.Variable, iteration: Option[variables.Variable]): Op

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    Attributes
    protected
    Definition Classes
    Optimizer

  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  9. val beta1: Double

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  10. val beta2: Double

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  11. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  12. def computeGradients(loss: Output, lossGradients: Seq[OutputLike] = null, variables: Set[variables.Variable] = null, gradientsGatingMethod: GatingMethod = Gradients.OpGating, gradientsAggregationMethod: AggregationMethod = Gradients.AddAggregationMethod, colocateGradientsWithOps: Boolean = false): Seq[(OutputLike, variables.Variable)]

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    Definition Classes
    Optimizer

  13. def createSlots(variables: Seq[variables.Variable]): Unit

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    Attributes
    protected
    Definition Classes
    Adam → Optimizer

  14. val decay: Decay

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  15. val epsilon: Double

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  16. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  17. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  18. def finish(updateOps: Set[Op], nameScope: String): Op

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    Attributes
    protected
    Definition Classes
    Adam → Optimizer

  19. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  20. def getSlot(name: String, variable: variables.Variable): variables.Variable

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    Attributes
    protected
    Definition Classes
    Optimizer

  21. def getSlot(name: String, variable: variables.Variable, initializer: Initializer, shape: core.Shape, dataType: types.DataType, variableScope: String): variables.Variable

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    Attributes
    protected
    Definition Classes
    Optimizer

  22. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  23. val learningRate: Double

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  24. val learningRateSummaryTag: String

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  25. def minimize(loss: Output, lossGradients: Seq[OutputLike] = null, variables: Set[variables.Variable] = null, gradientsGatingMethod: GatingMethod = Gradients.OpGating, gradientsAggregationMethod: AggregationMethod = Gradients.AddAggregationMethod, colocateGradientsWithOps: Boolean = false, iteration: Option[variables.Variable] = None, name: String = "Minimize"): Op

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    Definition Classes
    Optimizer

  26. val name: String

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    Definition Classes
    Adam → Optimizer

  27. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  28. final def notify(): Unit

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    Definition Classes
    AnyRef

  29. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  30. def prepare(iteration: Option[variables.Variable]): Unit

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    Definition Classes
    Adam → Optimizer

  31. def slotNames: Set[String]

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    Attributes
    protected
    Definition Classes
    Optimizer

  32. val slots: Map[String, Map[variables.Variable, variables.Variable]]

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    Attributes
    protected
    Definition Classes
    Optimizer

  33. val supportedDataTypes: Set[types.DataType]

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    Attributes
    protected
    Definition Classes
    Optimizer

  34. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  35. val useLocking: Boolean

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    Definition Classes
    Adam → Optimizer

  36. val useNesterov: Boolean

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  37. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  38. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  39. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  40. def zerosSlot(name: String, variable: variables.Variable, variableScope: String): variables.Variable

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    Attributes
    protected
    Definition Classes
    Optimizer

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Optimizer

Inherited from AnyRef

Inherited from Any

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