lstm
Syntax
Description
The long short-term memory (LSTM) operation allows a network to learn long-term dependencies between time steps in time series and sequence data.
Note
This function applies the deep learning LSTM operation to dlarray
data. If
you want to apply an LSTM operation within a layerGraph
object
or Layer
array, use
the following layer:
applies a long short-term memory (LSTM) calculation to input Y
= lstm(X
,H0
,C0
,weights
,recurrentWeights
,bias
)X
using the
initial hidden state H0
, initial cell state C0
, and
parameters weights
, recurrentWeights
, and
bias
. The input X
must be a formatted
dlarray
. The output Y
is a formatted
dlarray
with the same dimension format as X
, except
for any 'S'
dimensions.
The lstm
function updates the cell and hidden states using the
hyperbolic tangent function (tanh) as the state activation function. The
lstm
function uses the sigmoid function given by as the gate activation function.
[
also returns the hidden state and cell state after the LSTM operation.Y
,hiddenState
,cellState
] = lstm(X
,H0
,C0
,weights
,recurrentWeights
,bias
)
[___] = lstm(___,'DataFormat',
also specifies the dimension format FMT
)FMT
when X
is not
a formatted dlarray
. The output Y
is an unformatted
dlarray
with the same dimension order as X
, except
for any 'S'
dimensions.
Examples
Input Arguments
Output Arguments
Algorithms
Extended Capabilities
Version History
Introduced in R2019b
See Also
dlarray
| fullyconnect
| softmax
| dlgradient
| dlfeval
| gru
| attention