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sequenceInputLayer

Sequence input layer

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Description

A sequence input layer inputs sequence data to a neural network and applies data normalization.

Creation

Syntax

layer = sequenceInputLayer(inputSize)
layer = sequenceInputLayer(inputSize,Name=Value)

Description

layer = sequenceInputLayer(inputSize) creates a sequence input layer and sets the InputSize property.

layer = sequenceInputLayer(inputSize,Name=Value) sets optional properties using one or more name-value arguments.

example

Input Arguments

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Size of the input, specified as a positive integer or a vector of positive integers.

  • For vector sequence input, inputSize is a scalar corresponding to the number of features.

  • For 1-D image sequence input, inputSize is vector of two elements [h c], where h is the image height and c is the number of channels of the image.

  • For 2-D image sequence input, inputSize is vector of three elements [h w c], where h is the image height, w is the image width, and c is the number of channels of the image.

  • For 3-D image sequence input, inputSize is vector of four elements [h w d c], where h is the image height, w is the image width, d is the image depth, and c is the number of channels of the image.

To specify the minimum sequence length of the input data, use the MinLength name-value argument.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

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: sequenceInputLayer(12,Name="seq1") creates a sequence input layer with input size 12 and name 'seq1'.

Minimum sequence length of input data, specified as a positive integer. When training or making predictions with the network, if the input data has fewer than MinLength time steps, then the software throws an error.

When you create a network that downsamples data in the time dimension, you must take care that the network supports your training data and any data for prediction. Some deep learning layers require that the input has a minimum sequence length. For example, a 1-D convolution layer requires that the input has at least as many time steps as the filter size.

As time series of sequence data propagates through a network, the sequence length can change. For example, downsampling operations such as 1-D convolutions can output data with fewer time steps than its input. This means that downsampling operations can cause later layers in the network to throw an error because the data has a shorter sequence length than the minimum length required by the layer.

When you train or assemble a network, the software automatically checks that sequences of length 1 can propagate through the network. Some networks might not support sequences of length 1, but can successfully propagate sequences of longer lengths. To check that a network supports propagating your training and expected prediction data, set the MinLength property to a value less than or equal to the minimum length of your data and the expected minimum length of your prediction data.

Tip

To prevent convolution and pooling layers from changing the size of the data, set the Padding option of the layer to "same" or "causal".

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Data normalization to apply every time data is forward propagated through the input layer, specified as one of the following:

  • "zerocenter" — Subtract the mean specified by Mean.

  • "zscore" — Subtract the mean specified by Mean and divide by StandardDeviation.

  • "rescale-symmetric" — Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • "rescale-zero-one" — Rescale the input to be in the range [0, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • "none" — Do not normalize the input data.

  • function handle — Normalize the data using the specified function. The function must be of the form Y = f(X), where X is the input data and the output Y is the normalized data.

If the input data is complex-valued and the SplitComplexInputs option is 0 (false), then the Normalization option must be "zerocenter", "zscore", "none", or a function handle. (since R2024a)

Before R2024a: To input complex-valued data into the network, the SplitComplexInputs option must be 1 (true).

Tip

The software, by default, automatically calculates the normalization statistics when you use the trainnet function. To save time when training, specify the required statistics for normalization and set the ResetInputNormalization option in trainingOptions to 0 (false).

The software applies normalization to all input elements, including padding values.

The SequenceInputLayer object stores the Normalization property as a character vector or a function handle.

Data Types: char | string | function_handle

Normalization dimension, specified as one of the following:

  • "auto" – If the ResetInputNormalization training option is 0 (false) and you specify any of the normalization statistics (Mean, StandardDeviation, Min, or Max), then normalize over the dimensions matching the statistics. Otherwise, recalculate the statistics at training time and apply channel-wise normalization.

  • "channel" – Channel-wise normalization.

  • "element" – Element-wise normalization.

  • "all" – Normalize all values using scalar statistics.

The SequenceInputLayer object stores the NormalizationDimension property as a character vector.

Mean for zero-center and z-score normalization, specified as a numeric array, or empty.

  • For vector sequence input, Mean must be a InputSize-by-1 vector of means per channel, a numeric scalar, or [].

  • For 2-D image sequence input, Mean must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of means per channel, a numeric scalar, or [].

  • For 3-D image sequence input, Mean must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of means per channel, a numeric scalar, or [].

To specify the Mean property, the Normalization property must be "zerocenter" or "zscore". If Mean is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the mean using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 0.

Mean can be complex-valued. (since R2024a) If Mean is complex-valued, then the SplitComplexInputs option must be 0 (false).

Before R2024a: Split the mean into real and imaginary parts and set split the input data into real and imaginary parts by setting the SplitComplexInputs option to 1 (true).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Standard deviation used for z-score normalization, specified as a numeric array, a numeric scalar, or empty.

  • For vector sequence input, StandardDeviation must be a InputSize-by-1 vector of standard deviations per channel, a numeric scalar, or [].

  • For 2-D image sequence input, StandardDeviation must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of standard deviations per channel, a numeric scalar, or [].

  • For 3-D image sequence input, StandardDeviation must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of standard deviations per channel, or a numeric scalar.

To specify the StandardDeviation property, the Normalization must be "zscore". If StandardDeviation is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the standard deviation using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 1.

StandardDeviation can be complex-valued. (since R2024a) If StandardDeviation is complex-valued, then the SplitComplexInputs option must be 0 (false).

Before R2024a: Split the standard deviation into real and imaginary parts and set split the input data into real and imaginary parts by setting the SplitComplexInputs option to 1 (true).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Minimum value for rescaling, specified as a numeric array, or empty.

  • For vector sequence input, Min must be a InputSize-by-1 vector of means per channel or a numeric scalar.

  • For 2-D image sequence input, Min must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of minima per channel, or a numeric scalar.

  • For 3-D image sequence input, Min must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of minima per channel, or a numeric scalar.

To specify the Min property, the Normalization must be "rescale-symmetric" or "rescale-zero-one". If Min is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the minimum value using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to -1 and 0 when Normalization is "rescale-symmetric" and "rescale-zero-one", respectively.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Maximum value for rescaling, specified as a numeric array, or empty.

  • For vector sequence input, Max must be a InputSize-by-1 vector of means per channel or a numeric scalar.

  • For 2-D image sequence input, Max must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of maxima per channel, a numeric scalar, or [].

  • For 3-D image sequence input, Max must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of maxima per channel, a numeric scalar, or [].

To specify the Max property, the Normalization must be "rescale-symmetric" or "rescale-zero-one". If Max is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the maximum value using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Flag to split input data into real and imaginary components specified as one of these values:

  • 0 (false) – Do not split input data.

  • 1 (true) – Split data into real and imaginary components.

When SplitComplexInputs is 1, then the layer outputs twice as many channels as the input data. For example, if the input data is complex-valued with numChannels channels, then the layer outputs data with 2*numChannels channels, where channels 1 through numChannels contain the real components of the input data and numChannels+1 through 2*numChannels contain the imaginary components of the input data. If the input data is real, then channels numChannels+1 through 2*numChannels are all zero.

If the input data is complex-valued and SplitComplexInputs is 0 (false), then the layer passes the complex-valued data to the next layers. (since R2024a)

Before R2024a: To input complex-valued data into a neural network, the SplitComplexInputs option of the input layer must be 1 (true).

For an example showing how to train a network with complex-valued data, see Train Network with Complex-Valued Data.

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to layers with the name "".

The SequenceInputLayer object stores the Name property as a character vector.

Data Types: char | string

Properties

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Sequence Input

Size of the input, specified as a positive integer or a vector of positive integers.

  • For vector sequence input, InputSize is a scalar corresponding to the number of features.

  • For 1-D image sequence input, InputSize is vector of two elements [h c], where h is the image height and c is the number of channels of the image.

  • For 2-D image sequence input, InputSize is vector of three elements [h w c], where h is the image height, w is the image width, and c is the number of channels of the image.

  • For 3-D image sequence input, InputSize is vector of four elements [h w d c], where h is the image height, w is the image width, d is the image depth, and c is the number of channels of the image.

To specify the minimum sequence length of the input data, use the MinLength property.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Minimum sequence length of input data, specified as a positive integer. When training or making predictions with the network, if the input data has fewer than MinLength time steps, then the software throws an error.

When you create a network that downsamples data in the time dimension, you must take care that the network supports your training data and any data for prediction. Some deep learning layers require that the input has a minimum sequence length. For example, a 1-D convolution layer requires that the input has at least as many time steps as the filter size.

As time series of sequence data propagates through a network, the sequence length can change. For example, downsampling operations such as 1-D convolutions can output data with fewer time steps than its input. This means that downsampling operations can cause later layers in the network to throw an error because the data has a shorter sequence length than the minimum length required by the layer.

When you train or assemble a network, the software automatically checks that sequences of length 1 can propagate through the network. Some networks might not support sequences of length 1, but can successfully propagate sequences of longer lengths. To check that a network supports propagating your training and expected prediction data, set the MinLength property to a value less than or equal to the minimum length of your data and the expected minimum length of your prediction data.

Tip

To prevent convolution and pooling layers from changing the size of the data, set the Padding option of the layer to "same" or "causal".

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Data normalization to apply every time data is forward propagated through the input layer, specified as one of the following:

  • "zerocenter" — Subtract the mean specified by Mean.

  • "zscore" — Subtract the mean specified by Mean and divide by StandardDeviation.

  • "rescale-symmetric" — Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • "rescale-zero-one" — Rescale the input to be in the range [0, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • "none" — Do not normalize the input data.

  • function handle — Normalize the data using the specified function. The function must be of the form Y = f(X), where X is the input data and the output Y is the normalized data.

If the input data is complex-valued and the SplitComplexInputs option is 0 (false), then the Normalization option must be "zerocenter", "zscore", "none", or a function handle. (since R2024a)

Before R2024a: To input complex-valued data into the network, the SplitComplexInputs option must be 1 (true).

Tip

The software, by default, automatically calculates the normalization statistics when you use the trainnet function. To save time when training, specify the required statistics for normalization and set the ResetInputNormalization option in trainingOptions to 0 (false).

The software applies normalization to all input elements, including padding values.

The SequenceInputLayer object stores this property as a character vector or a function handle.

Data Types: char | string | function_handle

Normalization dimension, specified as one of the following:

  • "auto" – If the ResetInputNormalization training option is 0 (false) and you specify any of the normalization statistics (Mean, StandardDeviation, Min, or Max), then normalize over the dimensions matching the statistics. Otherwise, recalculate the statistics at training time and apply channel-wise normalization.

  • "channel" – Channel-wise normalization.

  • "element" – Element-wise normalization.

  • "all" – Normalize all values using scalar statistics.

The SequenceInputLayer object stores this property as a character vector.

Mean for zero-center and z-score normalization, specified as a numeric array, or empty.

  • For vector sequence input, Mean must be a InputSize-by-1 vector of means per channel, a numeric scalar, or [].

  • For 2-D image sequence input, Mean must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of means per channel, a numeric scalar, or [].

  • For 3-D image sequence input, Mean must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of means per channel, a numeric scalar, or [].

To specify the Mean property, the Normalization property must be "zerocenter" or "zscore". If Mean is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the mean using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 0.

Mean can be complex-valued. (since R2024a) If Mean is complex-valued, then the SplitComplexInputs option must be 0 (false).

Before R2024a: Split the mean into real and imaginary parts and split the input data into real and imaginary parts by setting the SplitComplexInputs option to 1 (true).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Standard deviation used for z-score normalization, specified as a numeric array, a numeric scalar, or empty.

  • For vector sequence input, StandardDeviation must be a InputSize-by-1 vector of standard deviations per channel, a numeric scalar, or [].

  • For 2-D image sequence input, StandardDeviation must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of standard deviations per channel, a numeric scalar, or [].

  • For 3-D image sequence input, StandardDeviation must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of standard deviations per channel, or a numeric scalar.

To specify the StandardDeviation property, the Normalization must be "zscore". If StandardDeviation is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the standard deviation using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 1.

StandardDeviation can be complex-valued. (since R2024a) If StandardDeviation is complex-valued, then the SplitComplexInputs option must be 0 (false).

Before R2024a: Split the standard deviation into real and imaginary parts and split the input data into real and imaginary parts by setting the SplitComplexInputs option to 1 (true).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Minimum value for rescaling, specified as a numeric array, or empty.

  • For vector sequence input, Min must be a InputSize-by-1 vector of means per channel or a numeric scalar.

  • For 2-D image sequence input, Min must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of minima per channel, or a numeric scalar.

  • For 3-D image sequence input, Min must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of minima per channel, or a numeric scalar.

To specify the Min property, the Normalization must be "rescale-symmetric" or "rescale-zero-one". If Min is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the minimum value using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to -1 and 0 when Normalization is "rescale-symmetric" and "rescale-zero-one", respectively.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Maximum value for rescaling, specified as a numeric array, or empty.

  • For vector sequence input, Max must be a InputSize-by-1 vector of means per channel or a numeric scalar.

  • For 2-D image sequence input, Max must be a numeric array of the same size as InputSize, a 1-by-1-by-InputSize(3) array of maxima per channel, a numeric scalar, or [].

  • For 3-D image sequence input, Max must be a numeric array of the same size as InputSize, a 1-by-1-by-1-by-InputSize(4) array of maxima per channel, a numeric scalar, or [].

To specify the Max property, the Normalization must be "rescale-symmetric" or "rescale-zero-one". If Max is [], then the software automatically sets the property at training or initialization time:

  • The trainnet function calculates the maximum value using the training data, ignoring any padding values, and uses the resulting value.

  • The initialize function and the dlnetwork function when the Initialize option is 1 (true) sets the property to 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

This property is read-only.

Flag to split input data into real and imaginary components specified as one of these values:

  • 0 (false) – Do not split input data.

  • 1 (true) – Split data into real and imaginary components.

When SplitComplexInputs is 1, then the layer outputs twice as many channels as the input data. For example, if the input data is complex-valued with numChannels channels, then the layer outputs data with 2*numChannels channels, where channels 1 through numChannels contain the real components of the input data and numChannels+1 through 2*numChannels contain the imaginary components of the input data. If the input data is real, then channels numChannels+1 through 2*numChannels are all zero.

If the input data is complex-valued and SplitComplexInputs is 0 (false), then the layer passes the complex-valued data to the next layers. (since R2024a)

Before R2024a: To input complex-valued data into a neural network, the SplitComplexInputs option of the input layer must be 1 (true).

For an example showing how to train a network with complex-valued data, see Train Network with Complex-Valued Data.

Layer

Layer name, specified as a character vector or string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to layers with the name "".

The SequenceInputLayer object stores this property as a character vector.

Data Types: char | string

This property is read-only.

Number of inputs of the layer. The layer has no inputs.

Data Types: double

This property is read-only.

Input names of the layer. The layer has no inputs.

Data Types: cell

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

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Open Live Script

Create a sequence input layer with an input size of 12.

layer = sequenceInputLayer(12)
layer = 
  SequenceInputLayer with properties:

                      Name: ''
                 InputSize: 12
                 MinLength: 1
        SplitComplexInputs: 0

   Hyperparameters
             Normalization: 'none'
    NormalizationDimension: 'auto'

Include a sequence input layer in a Layer array.

inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;

layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer]
layers = 
  4x1 Layer array with layers:

     1   ''   Sequence Input    Sequence input with 12 dimensions
     2   ''   LSTM              LSTM with 100 hidden units
     3   ''   Fully Connected   9 fully connected layer
     4   ''   Softmax           softmax
Open Live Script

Create a sequence input layer for sequences of 224-224 RGB images with the name 'seq1'.

layer = sequenceInputLayer([224 224 3], 'Name', 'seq1')
layer = 
  SequenceInputLayer with properties:

                      Name: 'seq1'
                 InputSize: [224 224 3]
                 MinLength: 1
        SplitComplexInputs: 0

   Hyperparameters
             Normalization: 'none'
    NormalizationDimension: 'auto'
Open Live Script

Train a deep learning LSTM network for sequence-to-label classification.

Load the example data from WaveformData.mat. The data is a numObservations-by-1 cell array of sequences, where numObservations is the number of sequences. Each sequence is a numTimeSteps-by-numChannels numeric array, where numTimeSteps is the number of time steps of the sequence and numChannels is the number of channels of the sequence.

load WaveformData

Visualize some of the sequences in a plot.

numChannels = size(data{1},2);

idx = [3 4 5 12];
figure
tiledlayout(2,2)
for i = 1:4
    nexttile
    stackedplot(data{idx(i)},DisplayLabels="Channel "+string(1:numChannels))
    
    xlabel("Time Step")
    title("Class: " + string(labels(idx(i))))
end

View the class names.

classNames = categories(labels)
classNames = 4×1 cell
    {'Sawtooth'}
    {'Sine'    }
    {'Square'  }
    {'Triangle'}

Set aside data for testing. Partition the data into a training set containing 90% of the data and a test set containing the remaining 10% of the data. To partition the data, use the trainingPartitions function, attached to this example as a supporting file. To access this file, open the example as a live script.

numObservations = numel(data);
[idxTrain,idxTest] = trainingPartitions(numObservations, [0.9 0.1]);
XTrain = data(idxTrain);
TTrain = labels(idxTrain);

XTest = data(idxTest);
TTest = labels(idxTest);

Define the LSTM network architecture. Specify the input size as the number of channels of the input data. Specify an LSTM layer to have 120 hidden units and to output the last element of the sequence. Finally, include a fully connected with an output size that matches the number of classes, followed by a softmax layer.

numHiddenUnits = 120;
numClasses = numel(categories(TTrain));

layers = [ ...
    sequenceInputLayer(numChannels)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer]
layers = 
  4×1 Layer array with layers:

     1   ''   Sequence Input    Sequence input with 3 dimensions
     2   ''   LSTM              LSTM with 120 hidden units
     3   ''   Fully Connected   4 fully connected layer
     4   ''   Softmax           softmax

Specify the training options. Train using the Adam solver with a learn rate of 0.01 and a gradient threshold of 1. Set the maximum number of epochs to 200 and shuffle every epoch. The software, by default, trains on a GPU if one is available. Using a GPU requires Parallel Computing Toolbox and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).

options = trainingOptions("adam", ...
    MaxEpochs=200, ...
    InitialLearnRate=0.01,...
    Shuffle="every-epoch", ...
    GradientThreshold=1, ...
    Verbose=false, ...
    Metrics="accuracy", ...
    Plots="training-progress");

Train the LSTM network using the trainnet function. For classification, use cross-entropy loss.

net = trainnet(XTrain,TTrain,layers,"crossentropy",options);

Classify the test data. Specify the same mini-batch size used for training.

scores = minibatchpredict(net,XTest);
YTest = scores2label(scores,classNames);

Calculate the classification accuracy of the predictions.

acc = mean(YTest == TTest)
acc = 0.8700

Display the classification results in a confusion chart.

figure
confusionchart(TTest,YTest)

Open Live Script

To create an LSTM network for sequence-to-label classification, create a layer array containing a sequence input layer, an LSTM layer, a fully connected layer, and a softmax layer.

Set the size of the sequence input layer to the number of features of the input data. Set the size of the fully connected layer to the number of classes. You do not need to specify the sequence length.

For the LSTM layer, specify the number of hidden units and the output mode "last".

numFeatures = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer];

For an example showing how to train an LSTM network for sequence-to-label classification and classify new data, see Sequence Classification Using Deep Learning.

To create an LSTM network for sequence-to-sequence classification, use the same architecture as for sequence-to-label classification, but set the output mode of the LSTM layer to "sequence".

numFeatures = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,OutputMode="sequence")
    fullyConnectedLayer(numClasses)
    softmaxLayer];
Open Live Script

To create an LSTM network for sequence-to-one regression, create a layer array containing a sequence input layer, an LSTM layer, and a fully connected layer.

Set the size of the sequence input layer to the number of features of the input data. Set the size of the fully connected layer to the number of responses. You do not need to specify the sequence length.

For the LSTM layer, specify the number of hidden units and the output mode "last".

numFeatures = 12;
numHiddenUnits = 125;
numResponses = 1;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numResponses)];

To create an LSTM network for sequence-to-sequence regression, use the same architecture as for sequence-to-one regression, but set the output mode of the LSTM layer to "sequence".

numFeatures = 12;
numHiddenUnits = 125;
numResponses = 1;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,OutputMode="sequence")
    fullyConnectedLayer(numResponses)];

For an example showing how to train an LSTM network for sequence-to-sequence regression and predict on new data, see Sequence-to-Sequence Regression Using Deep Learning.

Open Live Script

You can make LSTM networks deeper by inserting extra LSTM layers with the output mode "sequence" before the LSTM layer. To prevent overfitting, you can insert dropout layers after the LSTM layers.

For sequence-to-label classification networks, the output mode of the last LSTM layer must be "last".

numFeatures = 12;
numHiddenUnits1 = 125;
numHiddenUnits2 = 100;
numClasses = 9;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits1,OutputMode="sequence")
    dropoutLayer(0.2)
    lstmLayer(numHiddenUnits2,OutputMode="last")
    dropoutLayer(0.2)
    fullyConnectedLayer(numClasses)
    softmaxLayer];

For sequence-to-sequence classification networks, the output mode of the last LSTM layer must be "sequence".

numFeatures = 12;
numHiddenUnits1 = 125;
numHiddenUnits2 = 100;
numClasses = 9;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits1,OutputMode="sequence")
    dropoutLayer(0.2)
    lstmLayer(numHiddenUnits2,OutputMode="sequence")
    dropoutLayer(0.2)
    fullyConnectedLayer(numClasses)
    softmaxLayer];

Algorithms

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Extended Capabilities

Version History

Introduced in R2017b

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See Also

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Topics