Method LSTM

LSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorFloat, RnnDirection, RnnActivation[], float[], float[], bool, float, RnnLayout)

Generates an output tensor by computing a one-layer long short-term memory (LSTM) on an input tensor.

Declaration
public override void LSTM(TensorFloat X, TensorFloat W, TensorFloat R, TensorFloat B, TensorInt sequenceLens, TensorFloat initialH, TensorFloat initialC, TensorFloat P, TensorFloat Y, TensorFloat Yh, TensorFloat Yc, RnnDirection direction, RnnActivation[] activations, float[] activationAlpha, float[] activationBeta, bool inputForget, float clip, RnnLayout layout)
Parameters
Type Name Description
TensorFloat X

The input sequences tensor.
TensorFloat W

The weights tensor for the gates of the LSTM.
TensorFloat R

The recurrent weights tensor for the gates of the LSTM.
TensorFloat B

The optional bias tensor for the input gate of the LSTM.
TensorInt sequenceLens

The optional 1D tensor specifying the lengths of the sequences in a batch.
TensorFloat initialH

The optional initial values tensor of the hidden neurons of the LSTM. If this is null, the layer uses 0.
TensorFloat initialC

The optional initial values tensor of the cells of the LSTM. If this is null, the layer uses 0.
TensorFloat P

The optional weight tensor for the peepholes of the LSTM. If this is null, the layer uses 0.
TensorFloat Y

The output tensor to be computed and filled with the concatenated intermediate output values of the hidden.
TensorFloat Yh

The output tensor to be computed and filled with the last output value of the hidden.
TensorFloat Yc

The output tensor to be computed and filled with the last output value of the cell.
RnnDirection direction

The direction of the LSTM as an RnnDirection.
RnnActivation[] activations

The activation functions of the LSTM as an array of RnnActivation.
float[] activationAlpha

The alpha values of the activation functions of the LSTM.
float[] activationBeta

The beta values of the activation functions of the LSTM.
bool inputForget

Whether to forget the input values in the LSTM. If this is false, the layer couples the input and forget gates.
float clip

The cell clip threshold of the LSTM.
RnnLayout layout

The layout of the tensors as an RnnLayout.
Overrides