Constructor LSTM

LSTM(string, string[], string[], int, RnnDirection, RnnActivation[], float[], float[], float, bool, RnnLayout)

Initializes and returns an instance of a Long Short-Term Memory Network (LSTM) recurrent layer.

Declaration
public LSTM(string name, string[] inputs, string[] outputs, int hiddenSize, RnnDirection direction, RnnActivation[] activations = null, float[] activationAlpha = null, float[] activationBeta = null, float clip = 3.4028235E+38, bool inputForget = false, RnnLayout layout = RnnLayout.SequenceFirst)
Parameters
Type Name Description
string name

The name to use for the first output tensor of the layer.
string[] inputs

The names to use for the input tensors of the layer [X, W, R, (B, sequenceLens, initialH, initialC, P)].

 X is the name of the input sequences tensor.

 W is the name of the weights tensor for the gates of the LSTM.

 R is the name of the recurrent weights tensor for the gates of the LSTM.

 B is the name of the optional bias tensor for the input gate of the LSTM.

 sequenceLens is the name of the optional 1D tensor specifying the lengths of the sequences in a batch.

 initialH is the name of the optional initial values tensor of the hidden neurons of the LSTM. If this is `null` then 0 is used.

 initialC is the name of the optional initial values tensor of the cells of the LSTM. If this is `null` then 0 is used.

 P is the name of the optional weight tensor for the peepholes of the LSTM. If this is `null` then 0 is used.///
string[] outputs

The names for the output tensors of the layer [Y, Y_h, Y_c].

 Y is the name of the concatenated intermediate output values tensor of the hidden neurons.

 Y_h is the name of the last output values tensor of the hidden neurons.

 Y_c is the name of the last output values tensor of the cells.
int hiddenSize

The number of neurons in the hidden layer of the LSTM.
RnnDirection direction

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

The activation functions of the LSTM as an array of RnnActivation. If this is null then the LSTM uses the corresponding defaults for the given activations.
float[] activationAlpha

The alpha values of the activation functions of the LSTM.

 If this is `null` then the LSTM uses [0, 0, 0...].
float[] activationBeta

The beta values of the activation functions of the LSTM.

 If this is `null` then the LSTM uses the corresponding defaults for the given activations.
float clip

The cell clip threshold of the LSTM. The default value is float.MaxValue.
bool inputForget

Whether to forget the input values in the LSTM. If this is false the input and forget gates are coupled. The default value is false.
RnnLayout layout

The layout of the tensors as an RnnLayout. The default value is RnnLayout.SequenceFirst.