com.intel.analytics.bigdl.optim

SGD

class SGD[T] extends OptimMethod[T]

A plain implementation of SGD

T

Linear Supertypes
OptimMethod[T], Serializable, Serializable, AnyRef, Any
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  1. SGD
  2. OptimMethod
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Instance Constructors

  1. new SGD(learningRate: Double = 0.001, learningRateDecay: Double = 0.0, weightDecay: Double = 0.0, momentum: Double = 0.0, dampening: Double = scala.Double.MaxValue, nesterov: Boolean = false, learningRateSchedule: LearningRateSchedule = ..., learningRates: Tensor[T] = null, weightDecays: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    learningRate

    learning rate

    learningRateDecay

    learning rate decay

    weightDecay

    weight decay

    momentum

    momentum

    dampening

    dampening for momentum

    nesterov

    enables Nesterov momentum

    learningRates

    1D tensor of individual learning rates

    weightDecays

    1D tensor of individual weight decays

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clearHistory(): Unit

    Clear the history information in the OptimMethod state

    Clear the history information in the OptimMethod state

    returns

    Definition Classes
    SGDOptimMethod
  8. def clone(): OptimMethod[T]

    clone OptimMethod

    clone OptimMethod

    returns

    Definition Classes
    OptimMethod → AnyRef
  9. var dampening: Double

    dampening for momentum

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

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def getHyperParameter(config: Table): String

    return an string of current hyperParameter.

    return an string of current hyperParameter.

    config

    a table contains the hyper parameter.

    Definition Classes
    SGDOptimMethod
  15. def getHyperParameter(): String

    return an string of current hyperParameter.

    return an string of current hyperParameter.

    Definition Classes
    SGDOptimMethod
  16. def getLearningRate(): Double

    get learning rate

    get learning rate

    returns

    Definition Classes
    SGDOptimMethod
  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  19. var learningRate: Double

    learning rate

  20. var learningRateDecay: Double

    learning rate decay

  21. var learningRateSchedule: LearningRateSchedule

  22. var learningRates: Tensor[T]

    1D tensor of individual learning rates

  23. def loadFromTable(config: Table): SGD.this.type

    load optimMethod parameters from Table

    load optimMethod parameters from Table

    config
    returns

    Definition Classes
    SGDOptimMethod
  24. var momentum: Double

    momentum

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

    Definition Classes
    AnyRef
  26. var nesterov: Boolean

    enables Nesterov momentum

  27. final def notify(): Unit

    Definition Classes
    AnyRef
  28. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  29. def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), x: Tensor[T]): (Tensor[T], Array[T])

    feval

    a function that takes a single input (X), the point of a evaluation, and returns f(X) and df/dX

    x

    the initial point

    returns

    the new x 1D tensor and the function list, evaluated before the update

    Definition Classes
    SGDOptimMethod
  30. def save(path: String, overWrite: Boolean = false): SGD.this.type

    save OptimMethod

    save OptimMethod

    path

    path

    overWrite

    whether to overwrite

    returns

    Definition Classes
    OptimMethod
  31. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  32. def toString(): String

    Definition Classes
    AnyRef → Any
  33. def updateHyperParameter(config: Table, state: Table): Unit

    Update hyper parameter.

    Update hyper parameter. We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method optimize() is only called on the executor side, the driver's hyper parameter is unchanged. So this method is using to update hyper parameter on the driver side.

    config

    config table.

    state

    state Table.

    returns

    A string.

    Definition Classes
    SGDOptimMethod
  34. def updateHyperParameter(): Unit

    Update hyper parameter.

    Update hyper parameter. We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method optimize() is only called on the executor side, the driver's hyper parameter is unchanged. So this method is using to update hyper parameter on the driver side.

    returns

    A string.

    Definition Classes
    SGDOptimMethod
  35. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. var weightDecay: Double

    weight decay

  39. var weightDecays: Tensor[T]

    1D tensor of individual weight decays

Deprecated Value Members

  1. def clearHistory(state: Table): Table

    Clear the history information in the state

    Clear the history information in the state

    state
    returns

    Definition Classes
    OptimMethod
    Annotations
    @deprecated
    Deprecated

    (Since version 0.2.0) Please use clearHistory() instead

  2. def optimize(feval: (Tensor[T]) ⇒ (T, Tensor[T]), parameter: Tensor[T], config: Table, state: Table = null): (Tensor[T], Array[T])

    Optimize the model parameter

    Optimize the model parameter

    feval

    a function that takes a single input (X), the point of a evaluation, and returns f(X) and df/dX

    parameter

    the initial point

    config

    a table with configuration parameters for the optimizer

    state

    a table describing the state of the optimizer; after each call the state is modified

    returns

    the new x vector and the function list, evaluated before the update

    Definition Classes
    OptimMethod
    Annotations
    @deprecated
    Deprecated

    (Since version 0.2.0) Please initialize OptimMethod with parameters when creating it instead of importing table

Inherited from OptimMethod[T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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