BidirectionalRNN

Instance Constructors

new BidirectionalRNN(name: String, cellFw: RNNCell[O, OS, S, SS], cellBw: RNNCell[O, OS, S, SS], initialStateFw: () ⇒ S = null, initialStateBw: () ⇒ S = null, timeMajor: Boolean = false, parallelIterations: Int = 32, swapMemory: Boolean = false, sequenceLengths: tensors.Tensor[types.DataType] = null)(implicit evO: Aux[O, OS], evS: Aux[S, SS])

name
Name scope (also acting as variable scope) for this layer.
cellFw
RNN cell to use for the forward direction.
cellBw
RNN cell to use for the backward direction.
initialStateFw
Initial state to use for the forward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state. Defaults to a zero state.
initialStateBw
Initial state to use for the backward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state. Defaults to a zero state.
timeMajor
Boolean value indicating whether the inputs are provided in time-major format (i.e., have shape [time, batch, depth]) or in batch-major format (i.e., have shape [batch, time, depth]).
parallelIterations
Number of RNN loop iterations allowed to run in parallel.
swapMemory
If true, GPU-CPU memory swapping support is enabled for the RNN loop.
sequenceLengths
Optional INT32 tensor with shape [batchSize] containing the sequence lengths for each row in the batch.

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
def +(other: Layer[O, (Tuple[O, S], Tuple[O, S])]): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

Definition Classes
Layer
def ++(others: Seq[Layer[O, (Tuple[O, S], Tuple[O, S])]]): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

Definition Classes
Layer
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def >>[S](other: Layer[(Tuple[O, S], Tuple[O, S]), S]): Compose[O, (Tuple[O, S], Tuple[O, S]), S]

Definition Classes
Layer
def apply(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

Definition Classes
Layer
final def asInstanceOf[T0]: T0

Definition Classes
Any
val cellBw: RNNCell[O, OS, S, SS]

RNN cell to use for the backward direction.
val cellFw: RNNCell[O, OS, S, SS]

RNN cell to use for the forward direction.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def compose[S](other: Layer[(Tuple[O, S], Tuple[O, S]), S]): Compose[O, (Tuple[O, S], Tuple[O, S]), S]

Definition Classes
Layer
def concatenate(others: Layer[O, (Tuple[O, S], Tuple[O, S])]*): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

Definition Classes
Layer
final def currentStep: ops.Output

Definition Classes
Layer
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def forward(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

Definition Classes
Layer
def forwardWithoutContext(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

Definition Classes
BidirectionalRNN → Layer
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getParameter(name: String, dataType: types.DataType, shape: core.Shape, initializer: Initializer = null, regularizer: Regularizer = null, trainable: Boolean = true, reuse: Reuse = ReuseOrCreateNew, collections: Set[Key[ops.variables.Variable]] = Set.empty, cachingDevice: (OpSpecification) ⇒ String = null): ops.Output

Definition Classes
Layer
def hashCode(): Int

Definition Classes
AnyRef → Any
val initialStateBw: () ⇒ S

Initial state to use for the backward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state.
Initial state to use for the backward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state. Defaults to a zero state.
val initialStateFw: () ⇒ S

Initial state to use for the forward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state.
Initial state to use for the forward RNN, which is a structure over tensors with shapes [batchSize, stateShape(i)(0), stateShape(i)(1), ...], where i corresponds to the index of the corresponding state. Defaults to a zero state.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val layerType: String

Definition Classes
BidirectionalRNN → Layer
def map[MR](mapFn: ((Tuple[O, S], Tuple[O, S])) ⇒ MR): Layer[O, MR]

Definition Classes
Layer
val name: String

Name scope (also acting as variable scope) for this layer.
Name scope (also acting as variable scope) for this layer.

Definition Classes
BidirectionalRNN → Layer
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parallelIterations: Int

Number of RNN loop iterations allowed to run in parallel.
val sequenceLengths: tensors.Tensor[types.DataType]

Optional INT32 tensor with shape [batchSize] containing the sequence lengths for each row in the batch.
val swapMemory: Boolean

If true, GPU-CPU memory swapping support is enabled for the RNN loop.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val timeMajor: Boolean

Boolean value indicating whether the inputs are provided in time-major format (i.e., have shape [time, batch, depth]) or in batch-major format (i.e., have shape [batch, time, depth]).
def toString(): String

Definition Classes
Layer → 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( ... )
def withConcatenatedOutputs: Layer[O, Tuple[O, (S, S)]]

Related Docs: object BidirectionalRNN | package rnn

class BidirectionalRNN[O, OS, S, SS] extends Layer[O, (Tuple[O, S], Tuple[O, S])]

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: Layer[O, (Tuple[O, S], Tuple[O, S])]): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

def ++(others: Seq[Layer[O, (Tuple[O, S], Tuple[O, S])]]): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

final def ==(arg0: Any): Boolean

def >>[S](other: Layer[(Tuple[O, S], Tuple[O, S]), S]): Compose[O, (Tuple[O, S], Tuple[O, S]), S]

def apply(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

final def asInstanceOf[T0]: T0

val cellBw: RNNCell[O, OS, S, SS]

val cellFw: RNNCell[O, OS, S, SS]

def clone(): AnyRef

def compose[S](other: Layer[(Tuple[O, S], Tuple[O, S]), S]): Compose[O, (Tuple[O, S], Tuple[O, S]), S]

def concatenate(others: Layer[O, (Tuple[O, S], Tuple[O, S])]*): Concatenate[O, (Tuple[O, S], Tuple[O, S])]

final def currentStep: ops.Output

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def forward(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

def forwardWithoutContext(input: O)(implicit mode: Mode): (Tuple[O, S], Tuple[O, S])

final def getClass(): Class[_]

def hashCode(): Int

val initialStateBw: () ⇒ S

val initialStateFw: () ⇒ S

final def isInstanceOf[T0]: Boolean

val layerType: String

def map[MR](mapFn: ((Tuple[O, S], Tuple[O, S])) ⇒ MR): Layer[O, MR]

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parallelIterations: Int

val sequenceLengths: tensors.Tensor[types.DataType]

val swapMemory: Boolean

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

val timeMajor: Boolean

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def withConcatenatedOutputs: Layer[O, Tuple[O, (S, S)]]

Inherited from Layer[O, (Tuple[O, S], Tuple[O, S])]

Inherited from AnyRef

Inherited from Any

Ungrouped