lstm

R2026b

Long short-term memory

Syntax

Y = lstm(X,H0,C0,weights,recurrentWeights,bias)

[Y,hiddenState,cellState] = lstm(X,H0,C0,weights,recurrentWeights,bias)

___ = lstm(___,Name=Value)

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 dlnetwork object, use lstmLayer.

Y = lstm(X,H0,C0,weights,recurrentWeights,bias) applies a long short-term memory (LSTM) calculation to input 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 $$\sigma (x)={(1+e^{-x})}^{-1}$$ as the gate activation function.

[Y,hiddenState,cellState] = lstm(X,H0,C0,weights,recurrentWeights,bias) also returns the hidden state and cell state after the LSTM operation.

___ = lstm(___,Name=Value) specifies additional options using one or more name-value arguments.

Examples

Apply LSTM Operation to Sequence Data

Perform an LSTM operation using three hidden units.

Create the input sequence data as 32 observations with 10 channels and a sequence length of 64

numFeatures = 10;
numObservations = 32;
sequenceLength = 64;

X = randn(numFeatures,numObservations,sequenceLength);
X = dlarray(X,"CBT");

Create the initial hidden and cell states with three hidden units. Use the same initial hidden state and cell state for all observations.

numHiddenUnits = 3;
H0 = zeros(numHiddenUnits,1);
C0 = zeros(numHiddenUnits,1);

Create the learnable parameters for the LSTM operation.

weights = dlarray(randn(4*numHiddenUnits,numFeatures),"CU");
recurrentWeights = dlarray(randn(4*numHiddenUnits,numHiddenUnits),"CU");
bias = dlarray(randn(4*numHiddenUnits,1),"C");

Perform the LSTM calculation

[Y,hiddenState,cellState] = lstm(X,H0,C0,weights,recurrentWeights,bias);

View the size and dimensions of the output.

size(Y)

ans = 1×3

     3    32    64

dims(Y)

ans = 
'CBT'

View the size of the hidden and cell states.

size(hiddenState)

ans = 1×2

     3    32

size(cellState)

ans = 1×2

     3    32

Input Arguments

X — Input data

dlarray | numeric array

Input data, specified as a formatted dlarray, an unformatted dlarray, or a numeric array. When X is not a formatted dlarray, you must specify the dimension label format using the DataFormat option. If X is a numeric array, at least one of H0, C0, weights, recurrentWeights, or bias must be a dlarray.

X must contain a sequence dimension labeled "T". If X has any spatial dimensions labeled "S", they are flattened into the "C" channel dimension. If X does not have a channel dimension, then one is added. If X has any unspecified dimensions labeled "U", they must be singleton.

H0 — Initial hidden state vector

dlarray | numeric array

Initial hidden state vector, specified as a formatted dlarray, an unformatted dlarray, or a numeric array.

If H0 is a formatted dlarray, it must contain a channel dimension labeled "C" and optionally a batch dimension labeled "B" with the same size as the "B" dimension of X. If H0 does not have a "B" dimension, the function uses the same hidden state vector for each observation in X.

The size of the "C" dimension determines the number of hidden units. The size of the "C" dimension of H0 must be equal to the size of the "C" dimensions of C0.

If H0 is a not a formatted dlarray, the size of the first dimension determines the number of hidden units and must be the same size as the first dimension or the "C" dimension of C0.

C0 — Initial cell state vector

dlarray | numeric array

Initial cell state vector, specified as a formatted dlarray, an unformatted dlarray, or a numeric array.

If C0 is a formatted dlarray, it must contain a channel dimension labeled 'C' and optionally a batch dimension labeled 'B' with the same size as the 'B' dimension of X. If C0 does not have a 'B' dimension, the function uses the same cell state vector for each observation in X.

The size of the 'C' dimension determines the number of hidden units. The size of the 'C' dimension of C0 must be equal to the size of the 'C' dimensions of H0.

If C0 is a not a formatted dlarray, the size of the first dimension determines the number of hidden units and must be the same size as the first dimension or the 'C' dimension of H0.

weights — Weights

dlarray | numeric array

Weights, specified as a formatted dlarray, an unformatted dlarray, or a numeric array.

Specify weights as a matrix of size 4*NumHiddenUnits-by-InputSize, where NumHiddenUnits is the size of the "C" dimension of both C0 and H0, and InputSize is the size of the "C" dimension of X multiplied by the size of each "S" dimension of X, where present.

If weights is a formatted dlarray, it must contain a "C" dimension of size 4*NumHiddenUnits and a "U" dimension of size InputSize.

recurrentWeights — Recurrent weights

dlarray | numeric array

Recurrent weights, specified as a formatted dlarray, an unformatted dlarray, or a numeric array.

Specify recurrentWeights as a matrix of size 4*NumHiddenUnits-by-NumHiddenUnits, where NumHiddenUnits is the size of the "C" dimension of both C0 and H0.

If recurrentWeights is a formatted dlarray, it must contain a "C" dimension of size 4*NumHiddenUnits and a "U" dimension of size NumHiddenUnits.

bias — Bias

dlarray vector | numeric vector

Bias, specified as a formatted dlarray, an unformatted dlarray, or a numeric array.

Specify bias as a vector of length 4*NumHiddenUnits, where NumHiddenUnits is the size of the "C" dimension of both C0 and H0.

If bias is a formatted dlarray, the nonsingleton dimension must be labeled with "C".

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: Y = lstm(X,H0,C0,weights,recurrentWeights,bias,DataFormat="CTB") applies the LSTM operation and specifies that the data has format "CTB" (channel, time, batch).

DataFormat — Description of data dimensions

character vector | string scalar

Description of the data dimensions, specified as a character vector or string scalar.

A deep learning data format is a string of characters, where each character describes the type of the corresponding data dimension. The characters are:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, suppose you have an array that represents a batch of sequences where the first, second, and third dimensions correspond to channels, observations, and time steps, respectively. You can describe the data as having the format "CBT" (channel, batch, time).

You can specify multiple dimensions labeled "S" or "U". You can use the labels "C", "B", and "T" at most once each. The software ignores singleton trailing "U" dimensions after the second dimension.

If the input data is not a formatted dlarray object, then you must specify the DataFormat option.

For more information, see Deep Learning Data Formats.

Data Types: char | string

StateActivationFunction — State activation function

"tanh" (default) | "softsign" | "relu"

Since R2024a

Activation function to update the cell and hidden state, specified as one of these values:

• "tanh" — Use the hyperbolic tangent function (tanh).

• "softsign" — Use the softsign function $$\text{softsign}(x)=\frac{x}{1+\left|x\right|}$$.

• "relu" — Use the rectified linear unit (ReLU) function $$\text{ReLU}(x)=\{\begin{array}{cc}x,& x>0\\ 0,& x\le 0\end{array}$$.

The software uses this option as the function $${\sigma }_c$$ in the calculations to update the cell and hidden state.

For more information, see the definition of Long Short-Term Memory Layer on the lstmLayer reference page.

GateActivationFunction — Gate activation function

"sigmoid" (default) | "hard-sigmoid"

Since R2024a

Activation function to apply to the gates, specified as one of these values:

• "sigmoid" — Use the sigmoid function, $$\sigma (x)={(1+e^{-x})}^{-1}$$.

• "hard-sigmoid" — Use the hard sigmoid function,

  $$\sigma (x)=\{\begin{array}{cc}\begin{array}{l}0\hfill \\ 0.2x+0.5\hfill \\ 1\hfill \end{array}& \begin{array}{l}\text{if }x<-2.5\hfill \\ \text{if}-2.5\le x\le 2.5\hfill \\ \text{if }x>2.5\hfill \end{array}\end{array}.$$

The software uses this option as the function $${\sigma }_g$$ in the calculations for the layer gates.

For more information, see the definition of Long Short-Term Memory Layer on the lstmLayer reference page.

Output Arguments

Y — LSTM output

dlarray

LSTM output, returned as a dlarray. The output Y has the same underlying data type as the input X.

If the input data X is a formatted dlarray, Y has the same dimension format as X, except for any "S" dimensions. If the input data is not a formatted dlarray, Y is an unformatted dlarray with the same dimension order as the input data.

The size of the "C" dimension of Y is the same as the number of hidden units, specified by the size of the "C" dimension of H0 or C0.

hiddenState — Hidden state vector

dlarray | numeric array

Hidden state vector for each observation, returned as a dlarray or a numeric array with the same data type as H0.

If the input H0 is a formatted dlarray, then the output hiddenState is a formatted dlarray with the format "CB".

cellState — Cell state vector

dlarray | numeric array

Cell state vector for each observation, returned as a dlarray or a numeric array. cellState is returned with the same data type as C0.

If the input C0 is a formatted dlarray, the output cellState is returned as a formatted dlarray with the format 'CB'.

Algorithms

Long Short-Term Memory

The LSTM operation allows a network to learn long-term dependencies between time steps in time series and sequence data. For more information, see the definition of Long Short-Term Memory Layer on the lstmLayer reference page.

Deep Learning Array Formats

Most deep learning networks and functions operate on different dimensions of the input data in different ways.

For example, an LSTM operation iterates over the time dimension of the input data, and a batch normalization operation normalizes over the batch dimension of the input data.

To provide input data with labeled dimensions or input data with additional layout information, you can use data formats.

A deep learning data format is a string of characters, where each character describes the type of the corresponding data dimension. The characters are:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, suppose you have an array that represents a batch of sequences where the first, second, and third dimensions correspond to channels, observations, and time steps, respectively. You can describe the data as having the format "CBT" (channel, batch, time).

To create formatted input data, create a dlarray object and specify the format using the second argument.

To provide additional layout information with unformatted data, specify the format using the DataFormat argument.

For more information, see Deep Learning Data Formats.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™. (since R2026b)

Usage notes and limitations:

• • These arguments must be of single underlying data type:

    • X

    • H0

    • C0

    • weights

    • recurrentWeights

    • bias

  • The dimensions that correspond to the spatial and channel dimensions of the arguments must be of fixed size.

Before R2026b: C/C++ code generation does not support the lstm function.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™. (since R2026b)

Refer to the usage notes and limitations in the C/C++ Code Generation section. The same usage notes and limitations apply to GPU code generation.

Before R2026b: GPU code generation does not support the lstm function.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The lstm function supports GPU array input with these usage notes and limitations:

• When at least one of the following input arguments is a gpuArray or a dlarray with underlying data of type gpuArray, this function runs on the GPU:

  • X

  • H0

  • C0

  • weights

  • recurrentWeights

  • bias

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2019b

R2026b: Code generation support

C/C++ and GPU code generation support the lstm function.

R2024a: Specify state and gate activation functions

Specify the state and gate activation functions using the StateActivationFunction and GateActivationFunction name-value arguments, respectively.