org.apache.mxnet.javaapi

RNNParam

Related Doc: package javaapi

class RNNParam extends AnyRef

This Param Object is specifically used for RNN

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Instance Constructors

  1. new RNNParam(data: NDArray, parameters: NDArray, state: NDArray, state_cell: 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)

    state_size

    size of the state for each layer

    num_layers

    number of stacked layers

    mode

    the type of RNN to compute

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  9. def getBidirectional(): Boolean

  10. final def getClass(): Class[_]

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  11. def getData(): NDArray

  12. def getLstm_state_clip_max(): Double

  13. def getLstm_state_clip_min(): Double

  14. def getLstm_state_clip_nan(): Boolean

  15. def getMode(): String

  16. def getNum_layers(): Integer

  17. def getOut(): mxnet.NDArray

  18. def getP(): Float

  19. def getParameters(): NDArray

  20. def getProjection_size(): Integer

  21. def getState(): NDArray

  22. def getState_cell(): NDArray

  23. def getState_outputs(): Boolean

  24. def getState_size(): Integer

  25. def hashCode(): Int

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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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  30. def setBidirectional(bidirectional: Boolean): RNNParam

    bidirectional

    whether to use bidirectional recurrent layers

  31. 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.

  32. 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.

  33. 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.

  34. def setOut(out: NDArray): RNNParam

  35. def setP(p: Float): RNNParam

    p

    drop rate of the dropout on the outputs of each RNN layer, except the last layer.

  36. def setProjection_size(projection_size: Integer): RNNParam

    projection_size

    size of project size

  37. def setState_outputs(state_outputs: Boolean): RNNParam

    state_outputs

    Whether to have the states as symbol outputs.

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

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  39. def toString(): String

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