sequenceInputLayer
Sequence input layer
Description
A sequence input layer inputs sequence data to a neural network and applies data normalization.
Creation
Description
creates a sequence input layer and sets the layer
= sequenceInputLayer(inputSize
)InputSize
property.
sets the optional layer
= sequenceInputLayer(inputSize
,Name,Value
)MinLength
, Normalization
, Mean
, and Name
properties using namevalue pairs. You can specify multiple namevalue pairs.
Enclose each property name in single quotes.
Properties
Sequence Input
InputSize
— Size of input
positive integer  vector of positive integers
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 1D image sequence input,
InputSize
is vector of two elements[h c]
, whereh
is the image height andc
is the number of channels of the image.For 2D image sequence input,
InputSize
is vector of three elements[h w c]
, whereh
is the image height,w
is the image width, andc
is the number of channels of the image.For 3D image sequence input,
InputSize
is vector of four elements[h w d c]
, whereh
is the image height,w
is the image width,d
is the image depth, andc
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
MinLength
— Minimum sequence length of input data
1
(default)  positive integer
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 1D 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 1D 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
Normalization
— Data normalization
'none'
(default)  'zerocenter'
 'zscore'
 'rescalesymmetric'
 'rescalezeroone'
 function handle
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 byMean
.'zscore'
— Subtract the mean specified byMean
and divide byStandardDeviation
.'rescalesymmetric'
— Rescale the input to be in the range [1, 1] using the minimum and maximum values specified byMin
andMax
, respectively.'rescalezeroone'
— Rescale the input to be in the range [0, 1] using the minimum and maximum values specified byMin
andMax
, 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 = func(X)
, whereX
is the input data and the outputY
is the normalized data.
Tip
The software, by default, automatically calculates the normalization statistics when using the
trainnet
and trainNetwork
functions. 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.
Data Types: char
 string
 function_handle
NormalizationDimension
— Normalization dimension
'auto'
(default)  'channel'
 'element'
 'all'
Normalization dimension, specified as one of the following:
'auto'
– If the training option isfalse
and you specify any of the normalization statistics (Mean
,StandardDeviation
,Min
, orMax
), then normalize over the dimensions matching the statistics. Otherwise, recalculate the statistics at training time and apply channelwise normalization.'channel'
– Channelwise normalization.'element'
– Elementwise normalization.'all'
– Normalize all values using scalar statistics.
Data Types: char
 string
Mean
— Mean for zerocenter and zscore normalization
[]
(default)  numeric array  numeric scalar
Mean for zerocenter and zscore normalization, specified as a numeric array, or empty.
For vector sequence input,
Mean
must be aInputSize
by1 vector of means per channel, a numeric scalar, or[]
.For 2D image sequence input,
Mean
must be a numeric array of the same size asInputSize
, a 1by1byInputSize(3)
array of means per channel, a numeric scalar, or[]
.For 3D image sequence input,
Mean
must be a numeric array of the same size asInputSize
, a 1by1by1byInputSize(4)
array of means per channel, a numeric scalar, or[]
.
If you specify the Mean
property,
then Normalization
must be
'zerocenter'
or 'zscore'
. If
Mean
is []
,
then the trainnet
and
trainNetwork
functions calculate the mean and
ignores padding values. To train a dlnetwork
object
using a custom training loop or assemble a network without training it
using the assembleNetwork
function, you must set
the Mean
property to a numeric scalar or a numeric
array.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
StandardDeviation
— Standard deviation
[]
(default)  numeric array  numeric scalar
Standard deviation used for zscore normalization, specified as a numeric array, a numeric scalar, or empty.
For vector sequence input,
StandardDeviation
must be aInputSize
by1 vector of standard deviations per channel, a numeric scalar, or[]
.For 2D image sequence input,
StandardDeviation
must be a numeric array of the same size asInputSize
, a 1by1byInputSize(3)
array of standard deviations per channel, a numeric scalar, or[]
.For 3D image sequence input,
StandardDeviation
must be a numeric array of the same size asInputSize
, a 1by1by1byInputSize(4)
array of standard deviations per channel, or a numeric scalar.
If you specify the StandardDeviation
property, then Normalization
must be 'zscore'
. If
StandardDeviation
is
[]
, then the trainnet
and
trainNetwork
functions calculate the mean and
ignores padding values. To train a dlnetwork
object
using a custom training loop or assemble a network without training it
using the assembleNetwork
function, you must set
the StandardDeviation
property to a
numeric scalar or a numeric array.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
Min
— Minimum value for rescaling
[]
(default)  numeric array  numeric scalar
Minimum value for rescaling, specified as a numeric array, or empty.
For vector sequence input,
Min
must be aInputSize
by1 vector of means per channel or a numeric scalar.For 2D image sequence input,
Min
must be a numeric array of the same size asInputSize
, a 1by1byInputSize(3)
array of minima per channel, or a numeric scalar.For 3D image sequence input,
Min
must be a numeric array of the same size asInputSize
, a 1by1by1byInputSize(4)
array of minima per channel, or a numeric scalar.
If you specify the Min
property,
then Normalization
must be
'rescalesymmetric'
or
'rescalezeroone'
. If Min
is []
, then the
trainnet
and trainNetwork
functions calculate the minima and ignores padding values. To train a
dlnetwork
object using a custom training loop or
assemble a network without training it using the
assembleNetwork
function, you must set the
Min
property to a numeric scalar or a numeric
array.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
Max
— Maximum value for rescaling
[]
(default)  numeric array  numeric scalar
Maximum value for rescaling, specified as a numeric array, or empty.
For vector sequence input,
Max
must be aInputSize
by1 vector of means per channel or a numeric scalar.For 2D image sequence input,
Max
must be a numeric array of the same size asInputSize
, a 1by1byInputSize(3)
array of maxima per channel, a numeric scalar, or[]
.For 3D image sequence input,
Max
must be a numeric array of the same size asInputSize
, a 1by1by1byInputSize(4)
array of maxima per channel, a numeric scalar, or[]
.
If you specify the Max
property,
then Normalization
must be
'rescalesymmetric'
or
'rescalezeroone'
. If Max
is []
, then the
trainnet
and trainNetwork
functions calculate the maxima and ignores padding values. To train a
dlnetwork
object using a custom training loop or
assemble a network without training it using the
assembleNetwork
function, you must set the
Max
property to a numeric scalar or a numeric
array.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
SplitComplexInputs
— Flag to split input data into real and imaginary components
0
(false
) (default)  1
(true
)
This property is readonly.
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
complexvalues 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.
To input complexvalued data into a neural network, the
SplitComplexInputs
option of the input layer must be
1
.
For an example showing how to train a network with complexvalued data, see Train Network with ComplexValued Data.
Layer
Name
— Layer name
""
(default)  character vector  string scalar
Layer name, specified as a character vector or a string scalar.
For Layer
array input, the trainnet
, trainNetwork
, assembleNetwork
, layerGraph
, 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
NumInputs
— Number of inputs
0 (default)
This property is readonly.
Number of inputs of the layer. The layer has no inputs.
Data Types: double
InputNames
— Input names
{}
(default)
This property is readonly.
Input names of the layer. The layer has no inputs.
Data Types: cell
NumOutputs
— Number of outputs
1
(default)
This property is readonly.
Number of outputs from the layer, returned as 1
. This layer has a
single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is readonly.
Output names, returned as {'out'}
. This layer has a single output
only.
Data Types: cell
Examples
Create Sequence Input Layer
Create a sequence input layer with the name 'seq1'
and an input size of 12.
layer = sequenceInputLayer(12,'Name','seq1')
layer = SequenceInputLayer with properties: Name: 'seq1' 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 classificationLayer]
layers = 5x1 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 5 '' Classification Output crossentropyex
Create Sequence Input Layer for Image Sequences
Create a sequence input layer for sequences of 224224 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'
Train Network for Sequence Classification
Train a deep learning LSTM network for sequencetolabel classification.
Load the example data from WaveformData.mat
. The data is a numObservations
by1 cell array of sequences, where numObservations
is the number of sequences. Each sequence is a numChannels
bynumTimeSteps
numeric array, where numChannels
is the number of channels of the sequence and numTimeSteps
is the number of time steps of the sequence.
load WaveformData
Visualize some of the sequences in a plot.
numChannels = size(data{1},1); 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
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 and a classification layer.
numHiddenUnits = 120; numClasses = numel(categories(TTrain)); layers = [ ... sequenceInputLayer(numChannels) lstmLayer(numHiddenUnits,OutputMode="last") fullyConnectedLayer(numClasses) softmaxLayer classificationLayer]
layers = 5×1 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 5 '' Classification Output crossentropyex
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 150 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=150, ... InitialLearnRate=0.01,... Shuffle="everyepoch", ... GradientThreshold=1, ... Verbose=false, ... Plots="trainingprogress");
Train the LSTM network with the specified training options.
net = trainNetwork(XTrain,TTrain,layers,options);
Classify the test data. Specify the same minibatch size used for training.
YTest = classify(net,XTest);
Calculate the classification accuracy of the predictions.
acc = mean(YTest == TTest)
acc = 0.8400
Display the classification results in a confusion chart.
figure confusionchart(TTest,YTest)
Classification LSTM Networks
To create an LSTM network for sequencetolabel classification, create a layer array containing a sequence input layer, an LSTM layer, a fully connected layer, a softmax layer, and a classification output 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 classificationLayer];
For an example showing how to train an LSTM network for sequencetolabel classification and classify new data, see Sequence Classification Using Deep Learning.
To create an LSTM network for sequencetosequence classification, use the same architecture as for sequencetolabel 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 classificationLayer];
Regression LSTM Networks
To create an LSTM network for sequencetoone regression, create a layer array containing a sequence input layer, an LSTM layer, a fully connected layer, and a regression output 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) regressionLayer];
To create an LSTM network for sequencetosequence regression, use the same architecture as for sequencetoone 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) regressionLayer];
For an example showing how to train an LSTM network for sequencetosequence regression and predict on new data, see SequencetoSequence Regression Using Deep Learning.
Deeper LSTM Networks
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 sequencetolabel 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 classificationLayer];
For sequencetosequence 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 classificationLayer];
Create Network for Video Classification
Create a deep learning network for data containing sequences of images, such as video and medical image data.
To input sequences of images into a network, use a sequence input layer.
To apply convolutional operations independently to each time step, first convert the sequences of images to an array of images using a sequence folding layer.
To restore the sequence structure after performing these operations, convert this array of images back to image sequences using a sequence unfolding layer.
To convert images to feature vectors, use a flatten layer.
You can then input vector sequences into LSTM and BiLSTM layers.
Define Network Architecture
Create a classification LSTM network that classifies sequences of 28by28 grayscale images into 10 classes.
Define the following network architecture:
A sequence input layer with an input size of
[28 28 1]
.A convolution, batch normalization, and ReLU layer block with 20 5by5 filters.
An LSTM layer with 200 hidden units that outputs the last time step only.
A fully connected layer of size 10 (the number of classes) followed by a softmax layer and a classification layer.
To perform the convolutional operations on each time step independently, include a sequence folding layer before the convolutional layers. LSTM layers expect vector sequence input. To restore the sequence structure and reshape the output of the convolutional layers to sequences of feature vectors, insert a sequence unfolding layer and a flatten layer between the convolutional layers and the LSTM layer.
inputSize = [28 28 1]; filterSize = 5; numFilters = 20; numHiddenUnits = 200; numClasses = 10; layers = [ ... sequenceInputLayer(inputSize,'Name','input') sequenceFoldingLayer('Name','fold') convolution2dLayer(filterSize,numFilters,'Name','conv') batchNormalizationLayer('Name','bn') reluLayer('Name','relu') sequenceUnfoldingLayer('Name','unfold') flattenLayer('Name','flatten') lstmLayer(numHiddenUnits,'OutputMode','last','Name','lstm') fullyConnectedLayer(numClasses, 'Name','fc') softmaxLayer('Name','softmax') classificationLayer('Name','classification')];
Convert the layers to a layer graph and connect the miniBatchSize
output of the sequence folding layer to the corresponding input of the sequence unfolding layer.
lgraph = layerGraph(layers); lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');
View the final network architecture using the plot
function.
figure plot(lgraph)
Algorithms
Layer Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects. The format of a dlarray
object is a string of characters, in which each character describes the corresponding dimension of the data. The formats consists of one or more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can represent vector sequence data as a 3D array, in which the first
dimension corresponds to the channel dimension, the second dimension corresponds to the
batch dimension, and the third dimension corresponds to the time dimension. This
representation is in the format "CBT"
(channel, batch, time).
The input layer of a network specifies the layout of the data that the network expects. If you have data in a different layout, then specify the layout using the InputDataFormats
training option.
This table describes the expected layout of data for a neural network with a sequence input layer.
Data  Layout 

Vector sequences 

1D image sequences  hbycbyt arrays, where h and c correspond to the height and number of channels of the images, respectively, and t is the sequence length. 
2D image sequences  hbywbycbyt arrays, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, and t is the sequence length. 
3D image sequences  hbywbydbycbyt, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3D images, respectively, and t is the sequence length. 
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
For vector sequence inputs, the number of features must be a constant during code generation.
For code generation, the input data must contain either zero or two spatial dimensions.
Code generation does not support
'Normalization'
specified using a function handle.Code generation does not support complex input and does not support
'SplitComplexInputs'
option.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
To generate CUDA^{®} or C++ code by using GPU Coder™, you must first construct and train a deep neural network. Once the network is trained and evaluated, you can configure the code generator to generate code and deploy the convolutional neural network on platforms that use NVIDIA^{®} or ARM^{®} GPU processors. For more information, see Deep Learning with GPU Coder (GPU Coder).
For this layer, you can generate code that takes advantage of the NVIDIA CUDA deep neural network library (cuDNN), or the NVIDIA TensorRT™ high performance inference library.
The cuDNN library supports vector and 2D image sequences. The TensorRT library support only vector input sequences.
For vector sequence inputs, the number of features must be a constant during code generation.
For image sequence inputs, the height, width, and the number of channels must be a constant during code generation.
Code generation does not support
'Normalization'
specified using a function handle.Code generation does not support complex input and does not support
'SplitComplexInputs'
option.
Version History
Introduced in R2017bR2020a: trainNetwork
ignores padding values when calculating normalization statistics
Starting in R2020a, trainNetwork
ignores padding values when
calculating normalization statistics. This means that the Normalization
option in the
sequenceInputLayer
now makes training invariant to data
operations, for example, 'zerocenter'
normalization now implies
that the training results are invariant to the mean of the data.
If you train on padded sequences, then the calculated normalization factors may be different in earlier versions and can produce different results.
R2019b: sequenceInputLayer
, by default, uses channelwise normalization for zerocenter normalization
Starting in R2019b, sequenceInputLayer
, by default, uses
channelwise normalization for zerocenter normalization. In previous versions, this
layer uses elementwise normalization. To reproduce this behavior, set the NormalizationDimension
option of this layer to
'element'
.
See Also
trainnet
 trainNetwork
 lstmLayer
 bilstmLayer
 gruLayer
 classifyAndUpdateState
 predictAndUpdateState
 resetState
 sequenceFoldingLayer
 flattenLayer
 sequenceUnfoldingLayer
 Deep Network
Designer  featureInputLayer
Topics
 Sequence Classification Using Deep Learning
 Time Series Forecasting Using Deep Learning
 SequencetoSequence Classification Using Deep Learning
 Classify Videos Using Deep Learning
 Visualize Activations of LSTM Network
 Long ShortTerm Memory Neural Networks
 Specify Layers of Convolutional Neural Network
 Set Up Parameters and Train Convolutional Neural Network
 Deep Learning in MATLAB
 List of Deep Learning Layers
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