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Sequence input layer

A sequence input layer inputs sequence data to a network.

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 name-value pairs. You can specify multiple name-value pairs.
Enclose each property name in single quotes.

`InputSize`

— Size of inputpositive 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 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`

`MinLength`

— Minimum sequence length of input data`1`

(default) | positive integerMinimum 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`

`Normalization`

— Data normalization`'none'`

(default) | `'zerocenter'`

| `'zscore'`

| `'rescale-symmetric'`

| `'rescale-zero-one'`

| function handleData 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 = func(X)`

, where`X`

is the input data and the output`Y`

is the normalized data.

**Tip**

The software, by default, automatically calculates the normalization statistics at training time. To save time when training, specify the required statistics for normalization and set the `'ResetInputNormalization'`

option in `trainingOptions`

to `false`

.

If the input data contains padding, then the layer ignored padding values when normalizing the input data.

**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 is`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.

**Data Types: **`char`

| `string`

`Mean`

— Mean for zero-center and z-score normalization`[]`

(default) | numeric array | numeric scalarMean 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`[]`

.

If you specify the `Mean`

property,
then `Normalization`

must be
`'zerocenter'`

or `'zscore'`

. If
`Mean`

is `[]`

,
then the software calculates the mean at training time.

You can set this property when creating networks without
training (for example, when assembling networks using `assembleNetwork`

).

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`StandardDeviation`

— Standard deviation`[]`

(default) | numeric array | numeric scalarStandard 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.

If you specify the `StandardDeviation`

property,
then `Normalization`

must be
`'zscore'`

. If `StandardDeviation`

is `[]`

, then the software
calculates the standard deviation at training time.

You can set this property when creating networks without
training (for example, when assembling networks using `assembleNetwork`

).

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Min`

— Minimum value for rescaling`[]`

(default) | numeric array | numeric scalarMinimum 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.

If you specify the `Min`

property,
then `Normalization`

must be
`'rescale-symmetric'`

or
`'rescale-zero-one'`

. If `Min`

is `[]`

, then the software
calculates the minima at training time.

You can set this property when creating networks without
training (for example, when assembling networks using `assembleNetwork`

).

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Max`

— Maximum value for rescaling`[]`

(default) | numeric array | numeric scalarMaximum 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`[]`

.

If you specify the `Max`

property,
then `Normalization`

must be
`'rescale-symmetric'`

or
`'rescale-zero-one'`

. If `Max`

is `[]`

, then the software
calculates the maxima at training time.

`assembleNetwork`

).

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`Name`

— Layer name`''`

(default) | character vector | string scalar
Layer name, specified as a character vector or a string scalar.
For `Layer`

array input, the `trainNetwork`

,
`assembleNetwork`

, `layerGraph`

, and
`dlnetwork`

functions automatically assign names to layers with
`Name`

set to `''`

.

**Data Types: **`char`

| `string`

`NumInputs`

— Number of inputs0 (default)

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

**Data Types: **`double`

`InputNames`

— Input names`{}`

(default)Input names of the layer. The layer has no inputs.

**Data Types: **`cell`

`NumOutputs`

— Number of outputs`1`

(default)This property is read-only.

Number of outputs of the layer. This layer has a single output only.

**Data Types: **`double`

`OutputNames`

— Output names`{'out'}`

(default)This property is read-only.

Output names of the layer. This layer has a single output only.

**Data Types: **`cell`

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 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 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 Hyperparameters Normalization: 'none' NormalizationDimension: 'auto'

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

Load the Japanese Vowels data set as described in [1] and [2]. `XTrain`

is a cell array containing 270 sequences of varying length with 12 features corresponding to LPC cepstrum coefficients. `Y`

is a categorical vector of labels 1,2,...,9. The entries in `XTrain`

are matrices with 12 rows (one row for each feature) and a varying number of columns (one column for each time step).

[XTrain,YTrain] = japaneseVowelsTrainData;

Visualize the first time series in a plot. Each line corresponds to a feature.

figure plot(XTrain{1}') title("Training Observation 1") numFeatures = size(XTrain{1},1); legend("Feature " + string(1:numFeatures),'Location','northeastoutside')

Define the LSTM network architecture. Specify the input size as 12 (the number of features of the input data). Specify an LSTM layer to have 100 hidden units and to output the last element of the sequence. Finally, specify nine classes by including a fully connected layer of size 9, followed by a softmax layer and a classification layer.

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

Specify the training options. Specify the solver as `'adam'`

and `'GradientThreshold'`

as 1. Set the mini-batch size to 27 and set the maximum number of epochs to 70.

Because the mini-batches are small with short sequences, the CPU is better suited for training. Set `'ExecutionEnvironment'`

to `'cpu'`

. To train on a GPU, if available, set `'ExecutionEnvironment'`

to `'auto'`

(the default value).

maxEpochs = 70; miniBatchSize = 27; options = trainingOptions('adam', ... 'ExecutionEnvironment','cpu', ... 'MaxEpochs',maxEpochs, ... 'MiniBatchSize',miniBatchSize, ... 'GradientThreshold',1, ... 'Verbose',false, ... 'Plots','training-progress');

Train the LSTM network with the specified training options.

net = trainNetwork(XTrain,YTrain,layers,options);

Load the test set and classify the sequences into speakers.

[XTest,YTest] = japaneseVowelsTestData;

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

`YPred = classify(net,XTest,'MiniBatchSize',miniBatchSize);`

Calculate the classification accuracy of the predictions.

acc = sum(YPred == YTest)./numel(YTest)

acc = 0.9568

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, 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 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 classificationLayer];

To create an LSTM network for sequence-to-one 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 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) regressionLayer];

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.

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 classificationLayer];

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 classificationLayer];

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 28-by-28 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 5-by-5 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)

`sequenceInputLayer`

, by default, uses channel-wise normalization for zero-center normalization*Behavior change in future release*

Starting in R2019b, `sequenceInputLayer`

, by default, uses
channel-wise normalization for zero-center normalization. In previous versions, this
layer uses element-wise normalization. To reproduce this behavior, set the `NormalizationDimension`

option of this layer to
`'element'`

.

`sequenceInputLayer`

ignores padding values when normalizing*Behavior changed in R2020a*

Starting in R2020a, `sequenceInputLayer`

objects ignore padding
values in the input data when normalizing. 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.

[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." *Pattern Recognition Letters*. Vol. 20, No. 11–13, pages 1103–1111.

[2] *UCI Machine Learning Repository: Japanese Vowels
Dataset*.
https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

Generate C and C++ code using MATLAB® Coder™.

For vector sequence inputs, the number of features must be a constant during code generation.

Code generation does not support

`'Normalization'`

specified using a function handle.

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 2-D 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.

`lstmLayer`

| `bilstmLayer`

| `gruLayer`

| `classifyAndUpdateState`

| `predictAndUpdateState`

| `resetState`

| `sequenceFoldingLayer`

| `flattenLayer`

| `sequenceUnfoldingLayer`

| Deep Network
Designer | `featureInputLayer`

- Sequence Classification Using Deep Learning
- Time Series Forecasting Using Deep Learning
- Sequence-to-Sequence Classification Using Deep Learning
- Classify Videos Using Deep Learning
- Visualize Activations of LSTM Network
- Long Short-Term Memory 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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