Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  def +(other: String): String

   Implicit

   This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to any2stringadd[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method any2stringadd in scala.Predef.

   Definition Classes

   any2stringadd

4.  def ->[B](y: B): (AnyLayerOps[Input, OutputData, OutputDelta], B)

   Implicit

   This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to ArrowAssoc[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method ArrowAssoc in scala.Predef.

   Definition Classes

   ArrowAssoc

   Annotations

   @inline()

5.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

6.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

8.  def compose[G, NewInput <: Batch, InputData, InputDelta](g: G)(implicit toLayer: Aux[G, NewInput, InputData, InputDelta], toInput: <:<[Aux[NewInput, Aux[InputData, InputDelta]], Aux[NewInput, Input]]): Aux[NewInput, Aux[OutputData, OutputDelta]]

   Returns a Layer that accepts another layer's output as input of this layer

   Returns a Layer that accepts another layer's output as input of this layer

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableAny._
      def composeNetwork(implicit thisLayer: INDArray @Symbolic)(anotherLayer: INDArray @Symbolic) = {
        thisLayer.compose(anotherLayer)

9.  def ensuring(cond: (AnyLayerOps[Input, OutputData, OutputDelta]) ⇒ Boolean, msg: ⇒ Any): AnyLayerOps[Input, OutputData, OutputDelta]

   Implicit

   This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.

   Definition Classes

   Ensuring

10.  def ensuring(cond: (AnyLayerOps[Input, OutputData, OutputDelta]) ⇒ Boolean): AnyLayerOps[Input, OutputData, OutputDelta]

    Implicit

    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

11.  def ensuring(cond: Boolean, msg: ⇒ Any): AnyLayerOps[Input, OutputData, OutputDelta]

    Implicit

    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

12.  def ensuring(cond: Boolean): AnyLayerOps[Input, OutputData, OutputDelta]

    Implicit

    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.

    Definition Classes

    Ensuring

13.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

14.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

15.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

16.  def formatted(fmtstr: String): String

    Implicit

    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to StringFormat[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method StringFormat in scala.Predef.

    Definition Classes

    StringFormat

    Annotations

    @inline()

17.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

18.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

19.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

20.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

21.  final def notify(): Unit

    Definition Classes

    AnyRef

22.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

23.  def predict[InputData, InputDelta](inputData: InputData)(implicit ev: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Aux[InputData, InputDelta], Aux[OutputData, OutputDelta]]]): OutputData

    Return a Layer that accepts input and will only forward.

    Return a Layer that accepts input and will only forward. If you want to test the accuracy of network assertions, you can not let your network backward, then you need to use predict.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
       def composeNetwork(implicit input: INDArray @Symbolic) =???
       val predictor=composeNetwork
       predictor.predict(testData)

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

    Definition Classes

    AnyRef

25.  def toString(): String

    Definition Classes

    AnyRef → Any

26.  def train[InputData, InputDelta](inputData: InputData)(implicit ev: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Aux[InputData, InputDelta], Aux[OutputData, OutputDelta]]], outputDataIsOutputDelta: Trainable[OutputData, OutputDelta]): OutputData

    Return a Layer that accepts input and will forward & backward.

    Return a Layer that accepts input and will forward & backward. If you want to train your network,you need your network backward, then you need to use train.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
       def composeNetwork(implicit input: INDArray @Symbolic) =???
       val yourNetwork=composeNetwork
       yourNetwork.train(testData)

27.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

29.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

30.  def withOutputDataHook(hook: (OutputData) ⇒ Unit): Aux[Input, Aux[OutputData, OutputDelta]]

    In DeepLearning.Scala,operation is not immediately run, but first filled with placeholders, the entire network will be running ,then the real data will come into networks.

    In DeepLearning.Scala,operation is not immediately run, but first filled with placeholders, the entire network will be running ,then the real data will come into networks. So if you want to see some vars's intermediate state,you need to use withOutputDataHook.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
       (var:From[INDArray]##`@`).withOutputDataHook{ data => println(data) }

31.  def →[B](y: B): (AnyLayerOps[Input, OutputData, OutputDelta], B)

    Implicit

    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to ArrowAssoc[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method ArrowAssoc in scala.Predef.

    Definition Classes

    ArrowAssoc