sequenceInputLayer
Sequence input layer
Description
A sequence input layer inputs sequence data to a neural network and applies data normalization.
Creation
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 = f(X)
, whereX
is the input data and the outputY
is the normalized data.
If the input data is complexvalued 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 complexvalued 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
NormalizationDimension
— Normalization dimension
"auto"
(default)  "channel"
 "element"
 "all"
Normalization dimension, specified as one of the following:
"auto"
– If the training option is0
(false
) 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.
The SequenceInputLayer
object stores this property as a character vector.
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[]
.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to0
.
Mean
can be complexvalued. (since R2024a) If
Mean
is complexvalued, 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
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.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
StandardDeviation
can be
complexvalued. (since R2024a) If StandardDeviation
is complexvalued, 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
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.
To specify the Min
property, the
Normalization
must be
"rescalesymmetric"
or
"rescalezeroone"
. 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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
and0
whenNormalization
is"rescalesymmetric"
and"rescalezeroone"
, respectively.
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[]
.
To specify the Max
property, the
Normalization
must be
"rescalesymmetric"
or
"rescalezeroone"
. 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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
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
complexvalued 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 complexvalued and
SplitComplexInputs
is 0
(false
), then the layer passes the complexvalued data to the
next layers. (since R2024a)
Before R2024a: To input complexvalued 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 complexvalued data, see Train Network with ComplexValued Data.
Layer
Name
— Layer name
""
(default)  character vector  string scalar
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 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
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 numTimeSteps
bynumChannels
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="everyepoch", ... GradientThreshold=1, ... Verbose=false, ... Metrics="accuracy", ... Plots="trainingprogress");
Train the LSTM network using the trainnet
function. For classification, use crossentropy loss.
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);
Classify the test data. Specify the same minibatch 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)
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, 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 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];
Regression LSTM Networks
To create an LSTM network for sequencetoone 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 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)];
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];
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];
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 consist 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. 
Complex Numbers
For complexvalued input to the neural network, when the SplitComplexIputs
is 0
(false
), the layer passes complexvalued data to subsequent layers. (since R2024a)
Before R2024a: To input complexvalued data into a neural network, the SplitComplexInputs
option of the input layer must be 1
(true
).
If the input data is complexvalued and the SplitComplexInputs
option is 0
(false
), then the Normalization
option must be "zerocenter"
, "zscore"
, "none"
, or a function handle. The Mean
and StandardDeviation
properties of the layer also support complexvalued data for the "zerocenter"
and "zscore"
normalization options.
For an example showing how to train a network with complexvalued data, see Train Network with ComplexValued Data.
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.
You can generate C or C++ code that does not depend on any deep learning thirdparty libraries for input data with zero, one, two, or three spatial dimensions.
For ARM^{®} Compute and Intel^{®} MKLDNN, 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).
You can generate CUDA code that is independent of deep learning libraries for input data with zero, one, two, or three spatial dimensions.
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.
All spatial and channel dimensions of the input must be constant during code generation. For example,
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 R2017bR2024a: Complexvalued outputs
For complexvalued input to the neural network, when the SplitComplexIputs
is 0
(false
), the layer passes complexvalued data to subsequent layers.
If the input data is complexvalued and the SplitComplexInputs
option is
0
(false
), then the
Normalization
option must be "zerocenter"
,
"zscore"
, "none"
, or a function handle. The
Mean
and StandardDeviation
properties of the layer
also support complexvalued data for the "zerocenter"
and
"zscore"
normalization options.
R2024a: DAGNetwork
and SeriesNetwork
objects are not recommend
Starting in R2024a, DAGNetwork
and SeriesNetwork
objects are not recommended, use dlnetwork
objects
instead.
There are no plans to remove support for DAGNetwork
and
SeriesNetwork
objects. However, dlnetwork
objects have these advantages and are recommended instead:
dlnetwork
objects are a unified data type that supports network building, prediction, builtin training, visualization, compression, verification, and custom training loops.dlnetwork
objects support a wider range of network architectures that you can create or import from external platforms.The
trainnet
function supportsdlnetwork
objects, which enables you to easily specify loss functions. You can select from builtin loss functions or specify a custom loss function.Training and prediction with
dlnetwork
objects is typically faster thanLayerGraph
andtrainNetwork
workflows.
To convert a trained DAGNetwork
or SeriesNetwork
object to a dlnetwork
object, use the dag2dlnetwork
function.
Sequence input layers in a dlnetwork
object expect data in a
different layout when compared to sequence input layers in
DAGNetwork
or SeriesNetwork
objects. For
vector sequence input, DAGNetwork
and
SeriesNetwork
object functions expect
cbyt matrices, where
c is the number of features of the sequences and
t is the sequence length. For vector sequence input,
dlnetwork
object functions expect
tbyc matrices, where
t is the sequence length and c is the
number of features of the sequences.
R2020a: 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
 trainingOptions
 dlnetwork
 minibatchpredict
 predict
 scores2label
 lstmLayer
 bilstmLayer
 gruLayer
 sequenceFoldingLayer
 flattenLayer
 featureInputLayer
 Deep Network
Designer
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
 Deep Learning in MATLAB
 List of Deep Learning Layers
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