Uses of Class
org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMLayerWeights

Packages that use LSTMLayerWeights

Package	Description
org.nd4j.autodiff.samediff.ops
org.nd4j.linalg.api.ops.impl.layers.recurrent
org.nd4j.linalg.factory.ops

Uses of LSTMLayerWeights in org.nd4j.autodiff.samediff.ops

Methods in org.nd4j.autodiff.samediff.ops with parameters of type LSTMLayerWeights

Modifier and Type	Method	Description
SDVariable[]	SDRNN.lstmLayer(String[] names, SDVariable x, SDVariable cLast, SDVariable yLast, SDVariable 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
SDVariable[]	SDRNN.lstmLayer(String[] names, SDVariable 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
SDVariable[]	SDRNN.lstmLayer(SDVariable x, SDVariable cLast, SDVariable yLast, SDVariable 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
SDVariable[]	SDRNN.lstmLayer(SDVariable 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

Uses of LSTMLayerWeights in org.nd4j.linalg.api.ops.impl.layers.recurrent

Methods in org.nd4j.linalg.api.ops.impl.layers.recurrent with parameters of type LSTMLayerWeights

Modifier and Type	Method	Description
protected <T> boolean[]	LSTMLayer.bArgs(LSTMLayerWeights weights, T maxTSLength, T yLast, T cLast)
protected <T> boolean[]	LSTMLayerBp.bArgs(LSTMLayerWeights weights, T maxTSLength, T yLast, T cLast)

Constructors in org.nd4j.linalg.api.ops.impl.layers.recurrent with parameters of type LSTMLayerWeights

Constructor	Description
LSTMLayer(@NonNull SameDiff sameDiff, SDVariable x, SDVariable cLast, SDVariable yLast, SDVariable maxTSLength, LSTMLayerWeights weights, LSTMLayerConfig configuration)
LSTMLayer(INDArray x, INDArray cLast, INDArray yLast, INDArray maxTSLength, LSTMLayerWeights lstmWeights, LSTMLayerConfig LSTMLayerConfig)
LSTMLayerBp(@NonNull SameDiff sameDiff, @NonNull SDVariable x, SDVariable cLast, SDVariable yLast, SDVariable maxTSLength, @NonNull LSTMLayerWeights weights, @NonNull LSTMLayerConfig configuration, SDVariable dLdh, SDVariable dLdhL, SDVariable dLdcL)

Uses of LSTMLayerWeights in org.nd4j.linalg.factory.ops

Methods in org.nd4j.linalg.factory.ops with parameters of type LSTMLayerWeights

Modifier and Type	Method	Description
INDArray[]	NDRNN.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[]	NDRNN.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