featureInputLayer
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
Description
returns a feature input layer and sets the layer
= featureInputLayer(numFeatures
)InputSize
property to the specified number of features.
sets optional properties using one or more name-value arguments.layer
= featureInputLayer(numFeatures
,Name=Value
)
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
.
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 byMean
."zscore"
— Subtract the mean specified byMean
and divide byStandardDeviation
."rescale-symmetric"
— Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified byMin
andMax
, respectively."rescale-zero-one"
— 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 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 theResetInputNormalization
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 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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to0
.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to-1
and0
whenNormalization
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 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
)
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
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 byMean
."zscore"
— Subtract the mean specified byMean
and divide byStandardDeviation
."rescale-symmetric"
— Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified byMin
andMax
, respectively."rescale-zero-one"
— 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 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 theResetInputNormalization
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 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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to0
.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to1
.
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 thedlnetwork
function when theInitialize
option is1
(true
) sets the property to-1
and0
whenNormalization
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 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 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
NumInputs
— Number of inputs
0 (default)
This property is read-only.
Number of inputs of the layer. The layer has no inputs.
Data Types: double
InputNames
— Input names
{}
(default)
This property is read-only.
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 from the layer, returned as 1
. This layer has a
single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is read-only.
Output names, returned 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 = 3x1 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: [9x1 nnet.cnn.layer.Layer] Connections: [8x2 table] Learnables: [6x3 table] State: [0x3 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 = 2x1 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);
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 onehotdecode
function.
YTest = minibatchpredict(net,XTest); YTest = onehotdecode(YTest,classNames,2);
Visualize the predictions in a confusion chart.
confusionchart(TTest,YTest)
Calculate the classification accuracy, The accuracy is the proportion of the labels that the network predicts correctly.
accuracy = mean(YTest == TTest)
accuracy = 1
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 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 R2020bR2024a: 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.
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