java.lang.Object
- org.nd4j.linalg.factory.ops.NDRNN

```
public class NDRNN
extends Object
```

Constructor Summary

Constructors
Constructor Description

NDRNN()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`INDArray`	`gru(INDArray x, INDArray hLast, INDArray Wx, INDArray Wh, INDArray biases)`	The GRU operation.
`INDArray[]`	`gruCell(INDArray x, INDArray hLast, GRUWeights GRUWeights)`	The GRU cell.
`INDArray`	`lstmblock(INDArray maxTSLength, INDArray x, INDArray cLast, INDArray yLast, LSTMWeights LSTMWeights, LSTMConfiguration LSTMConfiguration)`	The LSTM block
`INDArray`	`lstmblock(INDArray x, LSTMWeights LSTMWeights, LSTMConfiguration LSTMConfiguration)`	The LSTM block
`INDArray[]`	`lstmCell(INDArray x, INDArray cLast, INDArray yLast, LSTMWeights LSTMWeights, LSTMConfiguration LSTMConfiguration)`	The LSTM cell.
`INDArray[]`	`lstmLayer(INDArray x, INDArray cLast, INDArray yLast, INDArray maxTSLength, LSTMLayerWeights LSTMLayerWeights, LSTMLayerConfig LSTMLayerConfig)`	Long Short-Term Memory layer - Hochreiter 1997. SUPPORTS following data formats: for unidirectional: TNS: shapes [timeLength, numExamples, inOutSize] NST: shapes [numExamples, inOutSize, timeLength] NTS: shapes [numExamples, timeLength, inOutSize] for bidirectional: T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX) SUPPORTS following direction modes: FWD: forward BWD: backward BIDIR_SUM: bidirectional sum BIDIR_CONCAT: bidirectional concat BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS) You may use different gate configurations: specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS") Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
`INDArray[]`	`lstmLayer(INDArray x, LSTMLayerWeights LSTMLayerWeights, LSTMLayerConfig LSTMLayerConfig)`	Long Short-Term Memory layer - Hochreiter 1997. SUPPORTS following data formats: for unidirectional: TNS: shapes [timeLength, numExamples, inOutSize] NST: shapes [numExamples, inOutSize, timeLength] NTS: shapes [numExamples, timeLength, inOutSize] for bidirectional: T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX) SUPPORTS following direction modes: FWD: forward BWD: backward BIDIR_SUM: bidirectional sum BIDIR_CONCAT: bidirectional concat BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS) You may use different gate configurations: specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS") Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
`INDArray`	`sru(INDArray x, INDArray initialC, INDArray mask, SRUWeights SRUWeights)`	The SRU layer.
`INDArray`	`sru(INDArray x, INDArray initialC, SRUWeights SRUWeights)`	The SRU layer.
`INDArray`	`sruCell(INDArray x, INDArray cLast, SRUWeights SRUWeights)`	The SRU layer.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - NDRNN
```
public NDRNN()
```
- Method Detail
  - gru
```
public INDArray gru(INDArray x,
                    INDArray hLast,
                    INDArray Wx,
                    INDArray Wh,
                    INDArray biases)
```
    The GRU operation. Gated Recurrent Unit - Cho et al. 2014.
    
    Parameters:
    
    x - input [time, bS, nIn] (NUMERIC type)
    
    hLast - initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
    
    Wx - input-to-hidden weights, [nIn, 3*nOut] (NUMERIC type)
    
    Wh - hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
    
    biases - biases, [3*nOut] (NUMERIC type)
    
    Returns:
    
    h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
  - gruCell
```
public INDArray[] gruCell(INDArray x,
                          INDArray hLast,
                          GRUWeights GRUWeights)
```
    The GRU cell. Does a single time step operation
    
    Parameters:
    
    x - Input, with shape [batchSize, inSize] (NUMERIC type)
    
    hLast - Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
    
    GRUWeights - Configuration Object
  - lstmCell
```
public INDArray[] lstmCell(INDArray x,
                           INDArray cLast,
                           INDArray yLast,
                           LSTMWeights LSTMWeights,
                           LSTMConfiguration LSTMConfiguration)
```
    The LSTM cell. Does a single time step operation.
    
    Parameters:
    
    x - Input, with shape [batchSize, inSize] (NUMERIC type)
    
    cLast - Previous cell state, with shape [batchSize, numUnits] (NUMERIC type)
    
    yLast - revious cell output, with shape [batchSize, numUnits] (NUMERIC type)
    
    LSTMWeights - Configuration Object
    
    LSTMConfiguration - Configuration Object
  - lstmLayer
```
public INDArray[] lstmLayer(INDArray x,
                            INDArray cLast,
                            INDArray yLast,
                            INDArray maxTSLength,
                            LSTMLayerWeights LSTMLayerWeights,
                            LSTMLayerConfig LSTMLayerConfig)
```
    Long Short-Term Memory layer - Hochreiter 1997.
    SUPPORTS following data formats:
    for unidirectional:
    TNS: shapes [timeLength, numExamples, inOutSize]
    NST: shapes [numExamples, inOutSize, timeLength]
    NTS: shapes [numExamples, timeLength, inOutSize]
    for bidirectional:
    T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
    SUPPORTS following direction modes:
    FWD: forward
    BWD: backward
    BIDIR_SUM: bidirectional sum
    BIDIR_CONCAT: bidirectional concat
    BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
    You may use different gate configurations:
    specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
    ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
    Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
    
    Parameters:
    
    x - Input, with shape dependent on the data format (in config). (NUMERIC type)
    
    cLast - Previous/initial cell state, with shape [batchSize, numUnits] (NUMERIC type)
    
    yLast - Previous/initial cell output, with shape [batchSize, numUnits] (NUMERIC type)
    
    maxTSLength - maxTSLength with shape [batchSize] (NUMERIC type)
    
    LSTMLayerWeights - Configuration Object
    
    LSTMLayerConfig - Configuration Object
  - lstmLayer
```
public INDArray[] lstmLayer(INDArray x,
                            LSTMLayerWeights LSTMLayerWeights,
                            LSTMLayerConfig LSTMLayerConfig)
```
    Long Short-Term Memory layer - Hochreiter 1997.
    SUPPORTS following data formats:
    for unidirectional:
    TNS: shapes [timeLength, numExamples, inOutSize]
    NST: shapes [numExamples, inOutSize, timeLength]
    NTS: shapes [numExamples, timeLength, inOutSize]
    for bidirectional:
    T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
    SUPPORTS following direction modes:
    FWD: forward
    BWD: backward
    BIDIR_SUM: bidirectional sum
    BIDIR_CONCAT: bidirectional concat
    BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
    You may use different gate configurations:
    specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
    ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
    Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
    
    Parameters:
    
    x - Input, with shape dependent on the data format (in config). (NUMERIC type)
    
    LSTMLayerWeights - Configuration Object
    
    LSTMLayerConfig - Configuration Object
  - lstmblock
```
public INDArray lstmblock(INDArray maxTSLength,
                          INDArray x,
                          INDArray cLast,
                          INDArray yLast,
                          LSTMWeights LSTMWeights,
                          LSTMConfiguration LSTMConfiguration)
```
    The LSTM block
    
    Parameters:
    
    maxTSLength - (NUMERIC type)
    
    x - Input, with shape dependent on the data format (in config). (NUMERIC type)
    
    cLast - Previous/initial cell state, with shape [batchSize, numUnits] (NUMERIC type)
    
    yLast - Previous/initial cell output, with shape [batchSize, numUnits] (NUMERIC type)
    
    LSTMWeights - Configuration Object
    
    LSTMConfiguration - Configuration Object
    
    Returns:
    
    output The layer's outputs. (NUMERIC type)
  - lstmblock
```
public INDArray lstmblock(INDArray x,
                          LSTMWeights LSTMWeights,
                          LSTMConfiguration LSTMConfiguration)
```
    The LSTM block
    
    Parameters:
    
    x - Input, with shape dependent on the data format (in config). (NUMERIC type)
    
    LSTMWeights - Configuration Object
    
    LSTMConfiguration - Configuration Object
    
    Returns:
    
    output The layer's outputs. (NUMERIC type)
  - sru
```
public INDArray sru(INDArray x,
                    INDArray initialC,
                    INDArray mask,
                    SRUWeights SRUWeights)
```
    The SRU layer. Does a single time step operation.
    
    Parameters:
    
    x - Input, with shape [batchSize, inSize] (NUMERIC type)
    
    initialC - Initial cell state, with shape [batchSize, inSize] (NUMERIC type)
    
    mask - An optional dropout mask, with shape [batchSize, inSize] (NUMERIC type)
    
    SRUWeights - Configuration Object
    
    Returns:
    
    output The cell's outputs.. (NUMERIC type)
  - sru
```
public INDArray sru(INDArray x,
                    INDArray initialC,
                    SRUWeights SRUWeights)
```
    The SRU layer. Does a single time step operation.
    
    Parameters:
    
    x - Input, with shape [batchSize, inSize] (NUMERIC type)
    
    initialC - Initial cell state, with shape [batchSize, inSize] (NUMERIC type)
    
    SRUWeights - Configuration Object
    
    Returns:
    
    output The cell's outputs.. (NUMERIC type)
  - sruCell
```
public INDArray sruCell(INDArray x,
                        INDArray cLast,
                        SRUWeights SRUWeights)
```
    The SRU layer. Does a single time step operation.
    
    Parameters:
    
    x - Input, with shape [batchSize, inSize] (NUMERIC type)
    
    cLast - Previous cell state, with shape [batchSize, inSize] (NUMERIC type)
    
    SRUWeights - Configuration Object
    
    Returns:
    
    output The cell's outputs. (NUMERIC type)

Class NDRNN

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

NDRNN

Method Detail

gru

gruCell

lstmCell

lstmLayer

lstmLayer

lstmblock

lstmblock

sru

sru

sruCell