RNNParam

Instance Constructors

new RNNParam(data: NDArray, parameters: NDArray, state: NDArray, state_cell: NDArray, sequence_length: NDArray, state_size: Integer, num_layers: Integer, mode: String)

data
Input data to RNN
parameters
Vector of all RNN trainable parameters concatenated
state
initial hidden state of the RNN
state_cell
initial cell state for LSTM networks (only for LSTM)
sequence_length
Vector of valid sequence lengths for each element in batch. (Only used if use_sequence_length kwarg is True)
state_size
size of the state for each layer
num_layers
number of stacked layers
mode
the type of RNN to compute

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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

Definition Classes
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def getData(): NDArray
def getLstm_state_clip_max(): Double
def getLstm_state_clip_min(): Double
def getLstm_state_clip_nan(): Boolean
def getMode(): String
def getNum_layers(): Integer
def getOut(): mxnet.NDArray
def getP(): Float
def getParameters(): NDArray
def getProjection_size(): Integer
def getSequence_length(): NDArray
def getState(): NDArray
def getState_cell(): NDArray
def getState_outputs(): Boolean
def getState_size(): Integer
def getUse_sequence_length(): Boolean
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def setBidirectional(bidirectional: Boolean): RNNParam

bidirectional
whether to use bidirectional recurrent layers
def setLstm_state_clip_max(lstm_state_clip_max: Double): RNNParam

lstm_state_clip_max
Maximum clip value of LSTM states. This option must be used together with lstm_state_clip_min.
def setLstm_state_clip_min(lstm_state_clip_min: Double): RNNParam

lstm_state_clip_min
Minimum clip value of LSTM states. This option must be used together with lstm_state_clip_max.
def setLstm_state_clip_nan(lstm_state_clip_nan: Boolean): RNNParam

lstm_state_clip_nan
Whether to stop NaN from propagating in state by clipping it to min/max. If clipping range is not specified, this option is ignored.
def setOut(out: NDArray): RNNParam
def setP(p: Float): RNNParam

p
drop rate of the dropout on the outputs of each RNN layer, except the last layer.
def setProjection_size(projection_size: Integer): RNNParam

projection_size
size of project size
def setState_outputs(state_outputs: Boolean): RNNParam

state_outputs
Whether to have the states as symbol outputs.
def setUse_sequence_length(use_sequence_length: Boolean): RNNParam

use_sequence_length
If set to true, this layer takes in an extra input parameter sequence_length to specify variable length sequence
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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Related Doc: package javaapi

class RNNParam extends AnyRef

Instance Constructors

new RNNParam(data: NDArray, parameters: NDArray, state: NDArray, state_cell: NDArray, sequence_length: NDArray, state_size: Integer, num_layers: Integer, mode: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def getBidirectional(): Boolean

final def getClass(): Class[_]

def getData(): NDArray

def getLstm_state_clip_max(): Double

def getLstm_state_clip_min(): Double

def getLstm_state_clip_nan(): Boolean

def getMode(): String

def getNum_layers(): Integer

def getOut(): mxnet.NDArray

def getP(): Float

def getParameters(): NDArray

def getProjection_size(): Integer

def getSequence_length(): NDArray

def getState(): NDArray

def getState_cell(): NDArray

def getState_outputs(): Boolean

def getState_size(): Integer

def getUse_sequence_length(): Boolean

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setBidirectional(bidirectional: Boolean): RNNParam

def setLstm_state_clip_max(lstm_state_clip_max: Double): RNNParam

def setLstm_state_clip_min(lstm_state_clip_min: Double): RNNParam

def setLstm_state_clip_nan(lstm_state_clip_nan: Boolean): RNNParam

def setOut(out: NDArray): RNNParam

def setP(p: Float): RNNParam

def setProjection_size(projection_size: Integer): RNNParam

def setState_outputs(state_outputs: Boolean): RNNParam

def setUse_sequence_length(use_sequence_length: Boolean): RNNParam

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

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