Implicits

Type Members

implicit final class FloatLayerOps[Operand0] extends AnyRef

Definition Classes
ImplicitsApi

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

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

Definition Classes
AnyRef → Any
implicit def Float*Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def Float+Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def Float-Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def Float/Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def abs(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

Definition Classes
ImplicitsApi
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
implicit def exp(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

Definition Classes
ImplicitsApi
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: <:<[From, Layers.Layer.Aux[Data0, Delta0]]): DeepLearning.Aux[From, Data0, Delta0]

Definition Classes
ImplicitsApi
implicit def log(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def max(Float,Float)[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
implicit def min(Float,Float)[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

Definition Classes
ImplicitsApi
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package RawFloatLayers

abstract type Implicits <: ImplicitsApi

Type Members

implicit final class FloatLayerOps[Operand0] extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

implicit def FloatFloat[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators..Case[Operand0, Operand1] { type Result = Out }

implicit def Float+Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

implicit def Float-Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

implicit def Float/Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

implicit def abs(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

implicit def exp(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: <:<[From, Layers.Layer.Aux[Data0, Delta0]]): DeepLearning.Aux[From, Data0, Delta0]

implicit def log(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from RawFloatLayers.ImplicitsApi

Inherited from AnyRef

Inherited from Any

Ungrouped

Implicits

Related Doc: package RawFloatLayers

abstract type Implicits <: ImplicitsApi

Type Members

implicit final class FloatLayerOps[Operand0] extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

implicit def Float*Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

implicit def Float+Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

implicit def Float-Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

implicit def Float/Float[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

implicit def abs(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

implicit def exp(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: <:<[From, Layers.Layer.Aux[Data0, Delta0]]): DeepLearning.Aux[From, Data0, Delta0]

implicit def log(Float)[Operand0, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from RawFloatLayers.ImplicitsApi

Inherited from AnyRef

Inherited from Any

Ungrouped

implicit def FloatFloat[Operand0, Operand1, Out <: FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators..Case[Operand0, Operand1] { type Result = Out }