RNN

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def bidirectionalDynamicRNN[O, OS, S, SS](cellFw: RNNCell[O, OS, S, SS], cellBw: RNNCell[O, OS, S, SS], input: O, initialStateFw: S = null.asInstanceOf[S], initialStateBw: S = null.asInstanceOf[S], timeMajor: Boolean = false, parallelIterations: Int = 32, swapMemory: Boolean = false, sequenceLengths: Output = null, name: String = "RNN")(implicit evO: Aux[O, OS], evS: Aux[S, SS]): (Tuple[O, S], Tuple[O, S])

   

   $OpDocRNNBidirectionalDynamicRNN

   $OpDocRNNBidirectionalDynamicRNN

   cellFw

   RNN cell to use for the forward direction.

   cellBw

   RNN cell to use for the backward direction.

   input

   Input to the RNN loop.

   initialStateFw

   Initial state to use for the forward RNN, which is a sequence of tensors with shapes [batchSize, stateSize(i)], where i corresponds to the index in that sequence. Defaults to a zero state.

   initialStateBw

   Initial state to use for the backward RNN, which is a sequence of tensors with shapes [batchSize, stateSize(i)], where i corresponds to the index in that sequence. 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.

   name

   Name prefix to use for the created ops.

   returns

   Tuple containing: (i) the forward RNN cell tuple after the forward dynamic RNN loop is completed, and (ii) the backward RNN cell tuple after the backward dynamic RNN loop is completed. The output of these tuples has a time axis prepended to the shape of each tensor and corresponds to the RNN outputs at each iteration in the loop. The state represents the RNN state at the end of the loop.

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   RNN

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   @throws( ... )

   Exceptions thrown

   InvalidShapeException If the inputs or the provided sequence lengths have invalid or unknown shapes.

6. def clone(): AnyRef

   

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7. def dynamicRNN[O, OS, S, SS](cell: RNNCell[O, OS, S, SS], input: O, initialState: S = null.asInstanceOf[S], timeMajor: Boolean = false, parallelIterations: Int = 32, swapMemory: Boolean = false, sequenceLengths: Output = null, name: String = "RNN")(implicit evO: Aux[O, OS], evS: Aux[S, SS]): Tuple[O, S]

   

   $OpDocRNNDynamicRNN

   $OpDocRNNDynamicRNN

   cell

   RNN cell to use.

   input

   Input to the RNN loop.

   initialState

   Initial state to use for the RNN, which is a sequence of tensors with shapes [batchSize, stateSize(i)], where i corresponds to the index in that sequence. 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.

   name

   Name prefix to use for the created ops.

   returns

   RNN cell tuple after the dynamic RNN loop is completed. The output of that tuple has a time axis prepended to the shape of each tensor and corresponds to the RNN outputs at each iteration in the loop. The state represents the RNN state at the end of the loop.

   Definition Classes

   RNN

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   @throws( ... ) @throws( ... )

   Exceptions thrown

   InvalidArgumentException If neither initialState nor zeroState is provided.

   InvalidShapeException If the inputs or the provided sequence lengths have invalid or unknown shapes.

8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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

    

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18. def toString(): String

    

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19. final def wait(): Unit

    

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

    

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21. final def wait(arg0: Long): Unit

    

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