featureInputLayer

Feature input layer

Since R2020b

Description

A feature input layer inputs feature data to a neural network and applies data normalization. Use this layer when you have a data set of numeric scalars representing features (data without spatial or time dimensions).

For image input, use imageInputLayer.

Creation

Syntax

layer = featureInputLayer(numFeatures)

layer = featureInputLayer(numFeatures,Name=Value)

Description

layer = featureInputLayer(numFeatures) returns a feature input layer and sets the InputSize property to the specified number of features.

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

Input Arguments

numFeatures — Number of features

positive integer

Number of features for each observation in the data, specified as a positive integer.

For image input, use imageInputLayer.

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: featureInputLayer(21,Name="input") creates a feature input layer with number of features set to 21 and name 'input'.

Normalization — Data normalization

"none" (default) | "zerocenter" | "zscore" | "rescale-symmetric" | "rescale-zero-one" | 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 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 FeatureInputLayer object stores the Normalization property as a character vector or a function handle.

NormalizationDimension — Normalization dimension

"auto" (default) | "channel" | "all"

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.

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

The FeatureInputLayer object stores this property as a character vector.

Mean — Mean for zero-center and z-score normalization

[] (default) | row vector | numeric scalar

Mean for zero-center and z-score normalization, specified as a 1-by-numFeatures vector of means per feature, 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 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

StandardDeviation — Standard deviation for z-score normalization

[] (default) | row vector | numeric scalar

Standard deviation for z-score normalization, specified as a 1-by-numFeatures vector of means per feature, a numeric scalar, or [].

To specify the StandardDeviation property, the Normalization property 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 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

Min — Minimum value for rescaling

[] (default) | row vector | numeric scalar

Minimum value for rescaling, specified as a 1-by-numFeatures vector of minima per feature, a numeric scalar, or [].

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 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

Max — Maximum value for rescaling

[] (default) | row vector | numeric scalar

Maximum value for rescaling, specified as a 1-by-numFeatures vector of maxima per feature, 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 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

SplitComplexInputs — Flag to split input data into real and imaginary components

0 (false) (default) | 1 (true)

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.

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 and dlnetwork functions automatically assign names to layers with the name "".

This argument sets the Name property.

Data Types: char | string

Properties

Feature Input

InputSize — Number of features

positive integer

Number of features for each observation in the data, specified as a positive integer.

For image input, use imageInputLayer.

Normalization — Data normalization

"none" (default) | "zerocenter" | "zscore" | "rescale-symmetric" | "rescale-zero-one" | 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 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 FeatureInputLayer object stores this property as a character vector or a function handle.

NormalizationDimension — Normalization dimension

"auto" (default) | "channel" | "all"

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.

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

The FeatureInputLayer object stores this property as a character vector.

Mean — Mean for zero-center and z-score normalization

[] (default) | row vector | numeric scalar

Mean for zero-center and z-score normalization, specified as a 1-by-numFeatures vector of means per feature, 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 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

StandardDeviation — Standard deviation for z-score normalization

[] (default) | row vector | numeric scalar

Standard deviation for z-score normalization, specified as a 1-by-numFeatures vector of means per feature, a numeric scalar, or [].

To specify the StandardDeviation property, the Normalization property 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 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

Min — Minimum value for rescaling

[] (default) | row vector | numeric scalar

Minimum value for rescaling, specified as a 1-by-numFeatures vector of minima per feature, a numeric scalar, or [].

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 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

Max — Maximum value for rescaling

[] (default) | row vector | numeric scalar

Maximum value for rescaling, specified as a 1-by-numFeatures vector of maxima per feature, 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 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

SplitComplexInputs — Flag to split input data into real and imaginary components

Read-only: 0 (false) (default) | 1 (true)

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

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 and dlnetwork functions automatically assign names to unnamed layers.

The FeatureInputLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

Read-only: 0 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

Read-only: {} (default)

This property is read-only.

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

Data Types: cell

NumOutputs — Number of outputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

OutputNames — Output names

Read-only: {'out'} (default)

This property is read-only.

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

Data Types: cell

Examples

Create Feature Input Layer

Create a feature input layer with the name "input" for observations consisting of 21 features.

layer = featureInputLayer(21,Name="input")

layer = 
  FeatureInputLayer with properties:

                      Name: 'input'
                 InputSize: 21
        SplitComplexInputs: 0

   Hyperparameters
             Normalization: 'none'
    NormalizationDimension: 'auto'

Include a feature input layer in a Layer array.

numFeatures = 21;
numClasses = 3;
 
layers = [
    featureInputLayer(numFeatures)
    fullyConnectedLayer(numClasses)
    softmaxLayer]

layers = 
  3×1 Layer array with layers:

     1   ''   Feature Input     21 features
     2   ''   Fully Connected   3 fully connected layer
     3   ''   Softmax           softmax

Combine Image and Feature Input Layers

Define the size of the input image, the number of features of each observation, the number of classes, and the size and number of filters of the convolution layer.

imageInputSize = [28 28 1];
numFeatures = 1;
numClasses = 10;
filterSize = 5;
numFilters = 16;

To create a network with two inputs, define the network in two parts and join them, for example, by using a concatenation layer.

Create a dlnetwork object.

net = dlnetwork;

Define the first part of the network. Define the image classification layers and include a flatten layer and a concatenation layer before the last fully connected layer.

layers = [
    imageInputLayer(imageInputSize,Normalization="none")
    convolution2dLayer(filterSize,numFilters,Name="conv")
    reluLayer
    fullyConnectedLayer(50)
    flattenLayer
    concatenationLayer(1,2,Name="concat")
    fullyConnectedLayer(numClasses)
    softmaxLayer];

net = addLayers(net,layers);

For the second part of the network, add a feature input layer and connect it to the second input of the concatenation layer.

featInput = featureInputLayer(numFeatures,Name="features");
net = addLayers(net,featInput);
net = connectLayers(net,"features","concat/in2")

net = 
  dlnetwork with properties:

         Layers: [9×1 nnet.cnn.layer.Layer]
    Connections: [8×2 table]
     Learnables: [6×3 table]
          State: [0×3 table]
     InputNames: {'imageinput'  'features'}
    OutputNames: {'softmax'}
    Initialized: 0

  View summary with summary.

Visualize the network.

plot(net)

Train Network with Tabular Data

If you have a data set of numeric features (for example tabular data without spatial or time dimensions), then you can train a deep neural network using a feature input layer.

Read the transmission casing data from the CSV file "transmissionCasingData.csv".

filename = "transmissionCasingData.csv";
tbl = readtable(filename,TextType="String");

Convert the labels for prediction to categorical using the convertvars function.

labelName = "GearToothCondition";
tbl = convertvars(tbl,labelName,"categorical");

To train a network using categorical features, you must first convert the categorical features to numeric. First, convert the categorical predictors to categorical using the convertvars function by specifying a string array containing the names of all the categorical input variables. In this data set, there are two categorical features with names "SensorCondition" and "ShaftCondition".

categoricalPredictorNames = ["SensorCondition" "ShaftCondition"];
tbl = convertvars(tbl,categoricalPredictorNames,"categorical");

Loop over the categorical input variables. For each variable, convert the categorical values to one-hot encoded vectors using the onehotencode function.

for i = 1:numel(categoricalPredictorNames)
    name = categoricalPredictorNames(i);
    tbl.(name) = onehotencode(tbl.(name),2);
end

View the first few rows of the table. Notice that the categorical predictors have been split into multiple columns.

head(tbl)

    SigMean     SigMedian    SigRMS    SigVar     SigPeak    SigPeak2Peak    SigSkewness    SigKurtosis    SigCrestFactor    SigMAD     SigRangeCumSum    SigCorrDimension    SigApproxEntropy    SigLyapExponent    PeakFreq    HighFreqPower    EnvPower    PeakSpecKurtosis    SensorCondition    ShaftCondition    GearToothCondition
    ________    _________    ______    _______    _______    ____________    ___________    ___________    ______________    _______    ______________    ________________    ________________    _______________    ________    _____________    ________    ________________    _______________    ______________    __________________

    -0.94876     -0.9722     1.3726    0.98387    0.81571       3.6314        -0.041525       2.2666           2.0514         0.8081        28562              1.1429             0.031581            79.931            0          6.75e-06       3.23e-07         162.13             0    1             1    0          No Tooth Fault  
    -0.97537    -0.98958     1.3937    0.99105    0.81571       3.6314        -0.023777       2.2598           2.0203        0.81017        29418              1.1362             0.037835            70.325            0          5.08e-08       9.16e-08         226.12             0    1             1    0          No Tooth Fault  
      1.0502      1.0267     1.4449    0.98491     2.8157       3.6314         -0.04162       2.2658           1.9487        0.80853        31710              1.1479             0.031565            125.19            0          6.74e-06       2.85e-07         162.13             0    1             0    1          No Tooth Fault  
      1.0227      1.0045     1.4288    0.99553     2.8157       3.6314        -0.016356       2.2483           1.9707        0.81324        30984              1.1472             0.032088             112.5            0          4.99e-06        2.4e-07         162.13             0    1             0    1          No Tooth Fault  
      1.0123      1.0024     1.4202    0.99233     2.8157       3.6314        -0.014701       2.2542           1.9826        0.81156        30661              1.1469              0.03287            108.86            0          3.62e-06       2.28e-07         230.39             0    1             0    1          No Tooth Fault  
      1.0275      1.0102     1.4338     1.0001     2.8157       3.6314         -0.02659       2.2439           1.9638        0.81589        31102              1.0985             0.033427            64.576            0          2.55e-06       1.65e-07         230.39             0    1             0    1          No Tooth Fault  
      1.0464      1.0275     1.4477     1.0011     2.8157       3.6314        -0.042849       2.2455           1.9449        0.81595        31665              1.1417             0.034159            98.838            0          1.73e-06       1.55e-07         230.39             0    1             0    1          No Tooth Fault  
      1.0459      1.0257     1.4402    0.98047     2.8157       3.6314        -0.035405       2.2757            1.955        0.80583        31554              1.1345               0.0353            44.223            0          1.11e-06       1.39e-07         230.39             0    1             0    1          No Tooth Fault  

View the class names of the data set.

classNames = categories(tbl{:,labelName})

classNames = 2×1 cell
    {'No Tooth Fault'}
    {'Tooth Fault'   }

Set aside data for testing. Partition the data into a training set containing 85% of the data and a test set containing the remaining 15% 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 = size(tbl,1);
[idxTrain,idxTest] = trainingPartitions(numObservations,[0.85 0.15]);

tblTrain = tbl(idxTrain,:);
tblTest = tbl(idxTest,:);

Convert the data to a format that the trainnet function supports. Convert the predictors and targets to numeric and categorical arrays, respectively. For feature input, the network expects data with rows that correspond to observations and columns that correspond to the features. If your data has a different layout, then you can preprocess your data to have this layout or you can provide layout information using data formats. For more information, see Deep Learning Data Formats.

predictorNames = ["SigMean" "SigMedian" "SigRMS" "SigVar" "SigPeak" "SigPeak2Peak" ...
    "SigSkewness" "SigKurtosis" "SigCrestFactor" "SigMAD" "SigRangeCumSum" ...
    "SigCorrDimension" "SigApproxEntropy" "SigLyapExponent" "PeakFreq" ...
    "HighFreqPower" "EnvPower" "PeakSpecKurtosis" "SensorCondition" "ShaftCondition"];
XTrain = table2array(tblTrain(:,predictorNames));
TTrain = tblTrain.(labelName);

XTest = table2array(tblTest(:,predictorNames));
TTest = tblTest.(labelName);

Define a network with a feature input layer and specify the number of features. Also, configure the input layer to normalize the data using Z-score normalization.

numFeatures = size(XTrain,2);
numClasses = numel(classNames);
 
layers = [
    featureInputLayer(numFeatures,Normalization="zscore")
    fullyConnectedLayer(16)
    layerNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer];

Specify the training options:

• Train using the L-BFGS solver. This solver suits tasks with small networks and when the data fits in memory.

• Train using the CPU. Because the network and data is small, the CPU is better suited.

• Display the training progress in a plot.

• Suppress the verbose output.

options = trainingOptions("lbfgs", ...
    ExecutionEnvironment="cpu", ...
    Plots="training-progress", ...
    Verbose=false);

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

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

Test the network using the labeled test set. For single-label classification, evaluate the accuracy. The accuracy is the percentage of the labels that the network predicts correctly.

accuracy = testnet(net,XTest,TTest,"accuracy")

accuracy = 
100

Predict the labels of the test data using the trained network. Predict the classification scores using the trained network then convert the predictions to labels using the scores2label function.

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

Visualize the predictions in a confusion chart.

confusionchart(TTest,YTest)

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 format consists of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, you can represent tabular data as a 2-D array, in which the first and second dimensions correspond to the batch and channel dimensions, respectively. This representation is in the format "BC" (batch, channel).

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.

The layer inputs N-by-c arrays to the network, where N and c are the numbers of observations and channels of the data, respectively. Data in this layout has the data format "BC" (batch, channel).

Extended Capabilities

C/C++ Code Generation

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

• Code generation does not support passing dlarray objects with unspecified (U) dimensions to this layer.

• 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™.

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).

• Code generation does not support passing dlarray objects with unspecified (U) dimensions to this layer.

• Code generation does not support complex input and does not support 'SplitComplexInputs' option.

Version History

Introduced in R2020b

R2024a: Complex-valued outputs

For complex-valued input to the neural network, when the SplitComplexIputs is 0 (false), the layer passes complex-valued data to subsequent layers.

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. The Mean and StandardDeviation properties of the layer also support complex-valued data for the "zerocenter" and "zscore" normalization options.