complexConvolution1dLayer

R2026b

Complex 1-D convolutional layer

Since R2026b

Description

A complex 1-D convolutional layer applies sliding complex convolutional filters to 1-D input. The layer convolves the input by first moving the filters along the input and computing the dot product of the weights and the input, and then adding a bias term.

The dimension that the layer convolves over depends on the layer input:

For time series and vector sequence input (data with three dimensions corresponding to the channels, observations, and time steps), the layer convolves over the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer convolves over the spatial dimension.
For 1-D image sequence input (data with four dimensions corresponding to the spatial pixels, channels, observations, and time steps), the layer convolves over the spatial dimension.

Creation

Syntax

layer = complexConvolution1dLayer(filterSize,numFilters)

layer = complexConvolution1dLayer(filterSize,numFilters,Name=Value)

Description

layer = complexConvolution1dLayer(filterSize,numFilters) creates a complex 1-D convolutional layer and sets the FilterSize and NumFilters properties.

example

layer = complexConvolution1dLayer(filterSize,numFilters,Name=Value) specifies additional options using one or more name-value arguments. For example, complexConvolution1dLayer(filterSize,numFilters,Padding=1) specifies padding of size 1 on the left and right of the layer input.

Input Arguments

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`filterSize` — Width of filters
positive integer

Width of the filters, specified as a positive integer.

This argument sets the FilterSize property.

`numFilters` — Number of filters
positive integer

Number of filters, specified as a positive integer. This number determines the number of channels (feature maps) in the layer output.

This argument sets the NumFilters property.

Name-Value Arguments

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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.

Example: complexConvolution1dLayer(11,96,Padding=1) creates a complex 1-D convolutional layer with 96 filters of size 11, and specifies padding of size 1 on the left and right of the layer input.

`Stride` — Step size for traversing input
`1` (default) | positive integer

Step size for traversing the input, specified as a positive integer.

This argument sets the Stride property.

`DilationFactor` — Factor for dilated convolution
`1` (default) | positive integer

Factor for dilated convolution (also known as atrous convolution), specified as a positive integer.

Use dilated convolutions to increase the receptive field (the area of the input that the layer can see) of the layer without increasing the number of parameters or computation.

The layer expands the filters by inserting zeros between each filter element. The dilation factor determines the step size for sampling the input, or equivalently, the upsampling factor of the filter. It corresponds to an effective filter size of (FilterSize – 1) .* DilationFactor + 1. For example, a 1-by-3 filter with a dilation factor of 2 is equivalent to a 1-by-5 filter with zeros between the elements.

This argument sets the DilationFactor property.

`Padding` — Padding to apply to input
`[0 0]` (default) | `"same"` | `"causal"` | nonnegative integer | vector of nonnegative integers

Padding to apply to the input, specified as one of the following:

"same" — Apply padding such that the output size is ceil(inputSize/Stride), where inputSize is the length of the input. When Stride is 1, the output is the same size as the input.
"causal" — Apply left padding to the input, equal to (FilterSize - 1) .* DilationFactor. When Stride is 1, the output is the same size as the input.
Nonnegative integer scalar sz — Add padding of size sz to both ends of the input.
Vector [l r] of nonnegative integers — Add padding of size l to the left of the input and r to the right of the input.

This argument sets the PaddingSize and PaddingMode properties.

Example: Padding=[2 1] adds padding of size 2 to the left of the input and size 1 to the right of the input.

`PaddingValue` — Value with which to pad input data
`0` (default) | scalar | `"symmetric-include-edge"` | `"symmetric-exclude-edge"` | `"replicate"`

Value with which to pad the input data, specified as one of these values:

`PaddingValue`	Description	Example
Scalar	Pad with the specified scalar value.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 0 & 0 & 3 & 1 & 4 & 0 & 0 \end{matrix}]$
`"symmetric-include-edge"`	Pad using mirrored values of the input, including the edge values.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 1 & 3 & 3 & 1 & 4 & 4 & 1 \end{matrix}]$
`"symmetric-exclude-edge"`	Pad using mirrored values of the input, excluding the edge values.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 4 & 1 & 3 & 1 & 4 & 1 & 3 \end{matrix}]$
`"replicate"`	Pad using repeated border elements of the input.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 3 & 3 & 3 & 1 & 4 & 4 & 4 \end{matrix}]$

This argument sets the PaddingValue property.

`NumChannels` — Number of input channels
`"auto"` (default) | positive integer

Number of input channels, specified as one of these values:

"auto" — Automatically determine the number of input channels at training time.
Positive integer — Configure the layer for the specified number of input channels. NumChannels and the number of channels in the layer input data must match. For example, if the input is an RGB image, then NumChannels must be 3. If the input is the output of a convolutional layer with 16 filters, then NumChannels must be 16.

This argument sets the NumChannels property.

`WeightsInitializer` — Function to initialize weights
`"complex-glorot-normal"` (default) | `"complex-glorot-uniform-square"` | `"complex-he-normal"` | `"complex-he-uniform-square"` | `"complex-narrow-normal"` | `"zeros"` | `"ones"` | function handle

Function to initialize the weights, specified as one of the following:

"complex-glorot-normal" – Initialize the weights with the complex normal Glorot initializer [1]. The complex normal Glorot initializer independently samples real and imaginary parts of the weights from a normal distribution with zero mean and variance 1/(numIn + numOut).
"complex-glorot-uniform-square" – Initialize the weights with the complex uniform square Glorot initializer [1]. The complex uniform square Glorot initializer independently samples real and imaginary parts of the weights from a uniform distribution in the interval (-sqrt(3/(numIn + numOut)), sqrt(3/(numIn + numOut))).
"complex-he-normal" – Initialize the weights with the complex normal He initializer [1]. The complex normal He initializer samples real and imaginary parts of the weights from a normal distribution with zero mean and variance 1/numIn.
"complex-he-uniform-square" – Initialize the weights with the complex uniform square He initializer [1]. The complex uniform square He initializer samples real and imaginary parts of the weights from a uniform distribution in the interval (-sqrt(3/numIn), sqrt(3/numIn)).
"complex-narrow-normal" — Initialize the real and imaginary parts of the weights by independently sampling from a normal distribution with a mean of zero and a variance of 1e-4/2, such that the total variance is 1e-4.
"zeros" – Initialize the weights with a real array of zeros.
"ones" – Initialize the weights with a real array of ones.
Function handle — Initialize the weights with a custom function. If you specify a function handle, then the function syntax must be of the form weights = func(sz), where sz is the size of the weights. For an example, see Specify Custom Weight Initialization Function.

Here, numIn = FilterSize*NumChannels and numOut = FilterSize*NumFilters.

The layer only initializes the weights when the Weights property is empty.

Data Types: char | string | function_handle

`BiasInitializer` — Function to initialize biases
`"zeros"` (default) | `"complex-narrow-normal"` | `"ones"` | function handle

Function to initialize the biases, specified as one of these values:

"zeros" — Initialize the biases with zeros.
"ones" — Initialize the biases with ones.
"complex-narrow-normal" — Initialize the real and imaginary parts of the weights by independently sampling from a normal distribution with a mean of zero and a variance of 1e-4/2, such that the total variance is 1e-4.
Function handle — Initialize the biases with a custom function. If you specify a function handle, then the function must have the form bias = func(sz), where sz is the size of the biases.

The layer only initializes the biases when the Bias property is empty.

Data Types: char | string | function_handle

`Weights` — Layer weights
`[]` (default) | numeric array

Layer weights for the convolution operation, specified as a FilterSize-by-NumChannels-by-NumFilters numeric array or [].

The layer weights are learnable parameters. You can specify the initial value of the weights directly using the Weights property of the layer. When you train a network, if the Weights property of the layer is nonempty, then the trainnet function uses the Weights property as the initial value. If the Weights property is empty, then the software uses the initializer specified by the WeightsInitializer property of the layer.

This argument sets the Weights property.

Data Types: single | double
Complex Number Support: Yes

`Bias` — Layer biases
`[]` (default) | numeric array

Layer biases for the convolution operation, specified as a 1-by-NumFilters numeric array or [].

The layer biases are learnable parameters. When you train a neural network, if Bias is nonempty, then the trainnet function uses the Bias property as the initial value. If Bias is empty, then software uses the initializer specified by the BiasInitializer property.

This argument sets the Bias property.

Data Types: single | double
Complex Number Support: Yes

`WeightLearnRateFactor` — Learning rate factor for weights
`1` (default) | nonnegative scalar

Learning rate factor for the weights, specified as a nonnegative scalar.

The software multiplies this factor by the global learning rate to determine the learning rate for the weights in this layer. For example, if WeightLearnRateFactor is 2, then the learning rate for the weights in this layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

This argument sets the WeightLearnRateFactor property.

`BiasLearnRateFactor` — Learning rate factor for biases
`1` (default) | nonnegative scalar

Learning rate factor for the biases, specified as a nonnegative scalar.

The software multiplies this factor by the global learning rate to determine the learning rate for the biases in this layer. For example, if BiasLearnRateFactor is 2, then the learning rate for the biases in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

This argument sets the BiasLearnRateFactor property.

`WeightL2Factor` — L₂ regularization factor for weights
`1` (default) | nonnegative scalar

L₂ regularization factor for the weights, specified as a nonnegative scalar.

The software multiplies this factor by the global L₂ regularization factor to determine the L₂ regularization for the weights in this layer. For example, if WeightL2Factor is 2, then the L₂ regularization for the weights in this layer is twice the global L₂ regularization factor. You can specify the global L₂ regularization factor using the trainingOptions function.

This argument sets the WeightL2Factor property.

`BiasL2Factor` — L₂ regularization factor for biases
`0` (default) | nonnegative scalar

L₂ regularization factor for the biases, specified as a nonnegative scalar.

The software multiplies this factor by the global L₂ regularization factor to determine the L₂ regularization for the biases in this layer. For example, if BiasL2Factor is 2, then the L₂ regularization for the biases in this layer is twice the global L₂ regularization factor. The software determines the global L₂ regularization factor based on the settings you specify using the trainingOptions function.

This argument sets the BiasL2Factor property.

`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.

This argument sets the Name property.

Data Types: char | string

Properties

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1-D Convolution

`FilterSize` — Width of filters
positive integer

This property is read-only after object creation. To set this property, use the corresponding positional input argument when you create the ComplexConvolution1DLayer object.

Width of the filters, specified as a positive integer.

Data Types: double

`NumFilters` — Number of filters
positive integer

This property is read-only after object creation. To set this property, use the corresponding positional input argument when you create the ComplexConvolution1DLayer object.

Number of filters, specified as a positive integer. This number determines the number of channels (feature maps) in the layer output.

Data Types: double

`Stride` — Step size for traversing input
`1` (default) | positive integer

Step size for traversing the input, specified as a positive integer.

If the stride size is less than the pooling window size, then the pooling regions overlap.

Data Types: double

`DilationFactor` — Factor for dilated convolution
`1` (default) | positive integer

Factor for dilated convolution (also known as atrous convolution), specified as a positive integer.

Use dilated convolutions to increase the receptive field (the area of the input that the layer can see) of the layer without increasing the number of parameters or computation.

The ComplexConvolution1DLayer object stores this property as double type.

`PaddingSize` — Size of padding
`[0 0]` (default) | vector of two nonnegative integers

Size of padding to apply to each side of the input, specified as a vector [l r] of two nonnegative integers, where l is the padding applied to the left and r is the padding applied to the right. To use the same value for both the left and right sizes, you can also specify a scalar value.

When you create a layer, use the Padding name-value argument to specify the padding size.

Data Types: double

`PaddingMode` — Method to determine padding size
`'manual'` (default) | `'same'` | `'causal'`

This property is read-only after object creation. To set this property, use the Padding name-value argument when you create the ComplexConvolution1DLayer object.

Method to determine padding size, specified as one of the following:

'manual' – Pad using the integer or vector specified by Padding.
'same' – Apply padding such that the output size is ceil(inputSize/Stride), where inputSize is the length of the input. When Stride is 1, the output is the same as the input.
'causal' – Apply causal padding. Pad the left of the input with padding size (FilterSize - 1) .* DilationFactor.

Data Types: char

`PaddingValue` — Value to pad data
`0` (default) | scalar | `"symmetric-include-edge"` | `"symmetric-exclude-edge"` | `"replicate"`

This property is read-only after object creation. To set this property, use the corresponding name-value argument when you create the ComplexConvolution1DLayer object.

Value to pad data, specified as one of these values:

`PaddingValue`	Description	Example
Scalar	Pad with the specified scalar value.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 0 & 0 & 3 & 1 & 4 & 0 & 0 \end{matrix}]$
`"symmetric-include-edge"`	Pad using mirrored values of the input, including the edge values.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 1 & 3 & 3 & 1 & 4 & 4 & 1 \end{matrix}]$
`"symmetric-exclude-edge"`	Pad using mirrored values of the input, excluding the edge values.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 4 & 1 & 3 & 1 & 4 & 1 & 3 \end{matrix}]$
`"replicate"`	Pad using repeated border elements of the input.	$[\begin{matrix} 3 & 1 & 4 \end{matrix}] \to [\begin{matrix} 3 & 3 & 3 & 1 & 4 & 4 & 4 \end{matrix}]$

The ComplexConvolution1DLayer object stores this property as a character vector or double type.

Data Types: double | char

`NumChannels` — Number of input channels
`"auto"` (default) | positive integer

This property is read-only after object creation. To set this property, use the corresponding name-value argument when you create the ComplexConvolution1DLayer object.

Number of input channels, specified as one of these values:

"auto" — Automatically determine the number of input channels at training time.
Positive integer — Configure the layer for the specified number of input channels. NumChannels and the number of channels in the layer input data must match. For example, if the input is an RGB image, then NumChannels must be 3. If the input is the output of a convolutional layer with 16 filters, then NumChannels must be 16.

The ComplexConvolution1DLayer object stores this property as a character vector or double type.

Data Types: double | char

Parameters and Initialization

`WeightsInitializer` — Function to initialize weights
`"complex-glorot-normal"` (default) | `"complex-glorot-uniform-square"` | `"complex-he-normal"` | `"complex-he-uniform-square"` | `"complex-narrow-normal"` | `"zeros"` | `"ones"` | function handle

Function to initialize the weights, specified as one of the following:

"complex-glorot-normal" – Initialize the weights with the complex normal Glorot initializer [1]. The complex normal Glorot initializer independently samples real and imaginary parts of the weights from a normal distribution with zero mean and variance 1/(numIn + numOut).
"complex-glorot-uniform-square" – Initialize the weights with the complex uniform square Glorot initializer [1]. The complex uniform square Glorot initializer independently samples real and imaginary parts of the weights from a uniform distribution in the interval (-sqrt(3/(numIn + numOut)), sqrt(3/(numIn + numOut))).
"complex-he-normal" – Initialize the weights with the complex normal He initializer [1]. The complex normal He initializer samples real and imaginary parts of the weights from a normal distribution with zero mean and variance 1/numIn.
"complex-he-uniform-square" – Initialize the weights with the complex uniform square He initializer [1]. The complex uniform square He initializer samples real and imaginary parts of the weights from a uniform distribution in the interval (-sqrt(3/numIn), sqrt(3/numIn)).
"complex-narrow-normal" — Initialize the real and imaginary parts of the weights by independently sampling from a normal distribution with a mean of zero and a variance of 1e-4/2, such that the total variance is 1e-4.
"zeros" – Initialize the weights with a real array of zeros.
"ones" – Initialize the weights with a real array of ones.
Function handle — Initialize the weights with a custom function. If you specify a function handle, then the function syntax must be of the form weights = func(sz), where sz is the size of the weights. For an example, see Specify Custom Weight Initialization Function.

Here, numIn = FilterSize*NumChannels and numOut = FilterSize*NumFilters.

The layer only initializes the weights when the Weights property is empty.

Data Types: char | string | function_handle

`BiasInitializer` — Function to initialize biases
`"zeros"` (default) | `"complex-narrow-normal"` | `"ones"` | function handle

Function to initialize the biases, specified as one of these values:

"zeros" — Initialize the biases with zeros.
"ones" — Initialize the biases with ones.
"complex-narrow-normal" — Initialize the real and imaginary parts of the weights by independently sampling from a normal distribution with a mean of zero and a variance of 1e-4/2, such that the total variance is 1e-4.
Function handle — Initialize the biases with a custom function. If you specify a function handle, then the function must have the form bias = func(sz), where sz is the size of the biases.

The layer only initializes the biases when the Bias property is empty.

Data Types: char | string | function_handle

`Weights` — Layer weights
`[]` (default) | numeric array

Layer weights for the convolution operation, specified as a FilterSize-by-NumChannels-by-NumFilters numeric array or [].

Data Types: single | double
Complex Number Support: Yes

`Bias` — Layer biases
`[]` (default) | numeric array

Layer biases for the convolution operation, specified as a 1-by-NumFilters numeric array or [].

Data Types: single | double
Complex Number Support: Yes

Learning Rate and Regularization

`WeightLearnRateFactor` — Learning rate factor for weights
`1` (default) | nonnegative scalar

Learning rate factor for the weights, specified as a nonnegative scalar.

Data Types: double

`BiasLearnRateFactor` — Learning rate factor for biases
`1` (default) | nonnegative scalar

Learning rate factor for the biases, specified as a nonnegative scalar.

The ComplexConvolution1DLayer object stores this property as double type.

`WeightL2Factor` — L₂ regularization factor for weights
`1` (default) | nonnegative scalar

L₂ regularization factor for the weights, specified as a nonnegative scalar.

Data Types: double

`BiasL2Factor` — L₂ regularization factor for biases
`0` (default) | nonnegative scalar

L₂ regularization factor for the biases, specified as a nonnegative scalar.

The ComplexConvolution1DLayer object stores this property as double type.

Layer

`Name` — Layer name
`''` (default) | character vector

Layer name, specified as a character vector. For Layer array input, the trainnet and dlnetwork functions automatically assign names to unnamed layers.

Data Types: char

`NumInputs` — Number of inputs
Read-only: `1` (default)

This property is read-only.

Number of inputs to the layer, represented as 1. This layer has a single input only.

Data Types: double

`InputNames` — Input name
Read-only: `{'in'}` (default)

This property is read-only.

Input name, represented as {'in'}. This layer has a single input only.

`NumOutputs` — Number of outputs
Read-only: `1` (default)

This property is read-only.

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

Data Types: double

`OutputNames` — Output name
Read-only: `{'out'}` (default)

This property is read-only.

Output name, represented as {'out'}. This layer has a single output only.

Examples

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Create Complex 1-D Convolutional Layer

Open Live Script

Create a complex 1-D convolutional layer with 96 filters of width 11 and the name "complex-conv1d".

layer = complexConvolution1dLayer(11,96,Name="complex-conv1d")

layer = 
  ComplexConvolution1DLayer with properties:

              Name: 'complex-conv1d'

   Hyperparameters
        FilterSize: 11
       NumChannels: 'auto'
        NumFilters: 96
            Stride: 1
    DilationFactor: 1
       PaddingMode: 'manual'
       PaddingSize: [0 0]
      PaddingValue: 0

   Learnable Parameters
           Weights: []
              Bias: []

  Show all properties

Algorithms

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Complex Numbers

Complex 1-D convolutional layers support real-valued and complex-valued inputs. The output is always complex, even if the inputs, weights, and biases are real.

Complex 1-D Convolutional Layer

A complex 1-D convolutional layer applies sliding complex convolutional filters to 1-D input. The layer convolves the input by moving the filters along the input and computing the dot product of the weights and the input, then adding a bias term.

The dimension that the layer convolves over depends on the layer input:

For time series and vector sequence input (data with three dimensions corresponding to the channels, observations, and time steps), the layer convolves over the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer convolves over the spatial dimension.
For 1-D image sequence input (data with four dimensions corresponding to the spatial pixels, channels, observations, and time steps), the layer convolves over the spatial dimension.

Layer Input and Output Formats

Most 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 vector sequence data as a 3-D 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).

You can interact with these dlarray objects in automatic differentiation workflows, such as those for:

developing a custom layer
using a functionLayer object
using the forward and predict functions with dlnetwork objects

This table shows the supported input formats of ComplexConvolution1DLayer objects and the corresponding output format. If the software passes the output of the layer to a custom layer that does not inherit from the nnet.layer.Formattable class, or to a FunctionLayer object with the Formattable property set to 0 (false), then the layer receives an unformatted dlarray object with dimensions ordered according to the formats in this table. The formats listed here are only a subset of the formats that the layer supports. The layer might support additional formats, such as formats with additional "S" (spatial) or "U" (unspecified) dimensions.

Input Format	Output Format
`"SCB"` (spatial, channel, batch)	`"SCB"` (spatial, channel, batch)
`"CBT"` (channel, batch, time)	`"CBT"` (channel, batch, time)
`"SCBT"` (spatial, channel, batch, time)	`"SCBT"` (spatial, channel, batch, time)
`"SC"` (spatial, channel)	`"SC"` (spatial, channel)
`"CT"` (channel, time)	`"CT"` (channel, time)
`"SCT"` (spatial, channel, time)	`"SCT"` (spatial, channel, time)

References

[1] Barrachina, Jose Agustin, Chengfang Ren, Gilles Vieillard, Christelle Morisseau, and Jean-Philippe Ovarlez. "Theory and Implementation of Complex-Valued Neural Networks". Preprint, submitted February 16, 2023. https://arxiv.org/abs/2302.08286

[2] Trabelsi, Chiheb, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal. "Deep Complex Networks". Preprint, submitted February 25, 2018. https://arxiv.org/abs/1705.09792.

Version History

Introduced in R2026b

complexConvolution1dLayer

Description

Creation

Syntax

Description

Input Arguments

filterSize — Width of filters positive integer

numFilters — Number of filters positive integer

Name-Value Arguments

Stride — Step size for traversing input 1 (default) | positive integer

DilationFactor — Factor for dilated convolution 1 (default) | positive integer

Padding — Padding to apply to input [0 0] (default) | "same" | "causal" | nonnegative integer | vector of nonnegative integers

PaddingValue — Value with which to pad input data 0 (default) | scalar | "symmetric-include-edge" | "symmetric-exclude-edge" | "replicate"

NumChannels — Number of input channels "auto" (default) | positive integer

WeightsInitializer — Function to initialize weights "complex-glorot-normal" (default) | "complex-glorot-uniform-square" | "complex-he-normal" | "complex-he-uniform-square" | "complex-narrow-normal" | "zeros" | "ones" | function handle

BiasInitializer — Function to initialize biases "zeros" (default) | "complex-narrow-normal" | "ones" | function handle

Weights — Layer weights [] (default) | numeric array

Bias — Layer biases [] (default) | numeric array

WeightLearnRateFactor — Learning rate factor for weights 1 (default) | nonnegative scalar

BiasLearnRateFactor — Learning rate factor for biases 1 (default) | nonnegative scalar

WeightL2Factor — L2 regularization factor for weights 1 (default) | nonnegative scalar

BiasL2Factor — L2 regularization factor for biases 0 (default) | nonnegative scalar

Name — Layer name "" (default) | character vector | string scalar

Properties

1-D Convolution

FilterSize — Width of filters positive integer

NumFilters — Number of filters positive integer

Stride — Step size for traversing input 1 (default) | positive integer

DilationFactor — Factor for dilated convolution 1 (default) | positive integer

PaddingSize — Size of padding [0 0] (default) | vector of two nonnegative integers

PaddingMode — Method to determine padding size 'manual' (default) | 'same' | 'causal'

PaddingValue — Value to pad data 0 (default) | scalar | "symmetric-include-edge" | "symmetric-exclude-edge" | "replicate"

NumChannels — Number of input channels "auto" (default) | positive integer

Parameters and Initialization

WeightsInitializer — Function to initialize weights "complex-glorot-normal" (default) | "complex-glorot-uniform-square" | "complex-he-normal" | "complex-he-uniform-square" | "complex-narrow-normal" | "zeros" | "ones" | function handle

BiasInitializer — Function to initialize biases "zeros" (default) | "complex-narrow-normal" | "ones" | function handle

Weights — Layer weights [] (default) | numeric array

Bias — Layer biases [] (default) | numeric array

Learning Rate and Regularization

WeightLearnRateFactor — Learning rate factor for weights 1 (default) | nonnegative scalar

BiasLearnRateFactor — Learning rate factor for biases 1 (default) | nonnegative scalar

WeightL2Factor — L2 regularization factor for weights 1 (default) | nonnegative scalar

BiasL2Factor — L2 regularization factor for biases 0 (default) | nonnegative scalar

Layer

Name — Layer name '' (default) | character vector

NumInputs — Number of inputs Read-only: 1 (default)

InputNames — Input name Read-only: {'in'} (default)

NumOutputs — Number of outputs Read-only: 1 (default)

OutputNames — Output name Read-only: {'out'} (default)

Examples

Create Complex 1-D Convolutional Layer

Algorithms

Complex Numbers

Complex 1-D Convolutional Layer

Layer Input and Output Formats

References

Version History

See Also

Topics

`filterSize` — Width of filters
positive integer

`numFilters` — Number of filters
positive integer

`Stride` — Step size for traversing input
`1` (default) | positive integer

`DilationFactor` — Factor for dilated convolution
`1` (default) | positive integer

`Padding` — Padding to apply to input
`[0 0]` (default) | `"same"` | `"causal"` | nonnegative integer | vector of nonnegative integers

`PaddingValue` — Value with which to pad input data
`0` (default) | scalar | `"symmetric-include-edge"` | `"symmetric-exclude-edge"` | `"replicate"`

`NumChannels` — Number of input channels
`"auto"` (default) | positive integer

`WeightsInitializer` — Function to initialize weights
`"complex-glorot-normal"` (default) | `"complex-glorot-uniform-square"` | `"complex-he-normal"` | `"complex-he-uniform-square"` | `"complex-narrow-normal"` | `"zeros"` | `"ones"` | function handle

`BiasInitializer` — Function to initialize biases
`"zeros"` (default) | `"complex-narrow-normal"` | `"ones"` | function handle

`Weights` — Layer weights
`[]` (default) | numeric array

`Bias` — Layer biases
`[]` (default) | numeric array

`WeightLearnRateFactor` — Learning rate factor for weights
`1` (default) | nonnegative scalar

`BiasLearnRateFactor` — Learning rate factor for biases
`1` (default) | nonnegative scalar

`WeightL2Factor` — L₂ regularization factor for weights
`1` (default) | nonnegative scalar

`BiasL2Factor` — L₂ regularization factor for biases
`0` (default) | nonnegative scalar

`Name` — Layer name
`""` (default) | character vector | string scalar

`FilterSize` — Width of filters
positive integer

`NumFilters` — Number of filters
positive integer

`Stride` — Step size for traversing input
`1` (default) | positive integer

`DilationFactor` — Factor for dilated convolution
`1` (default) | positive integer

`PaddingSize` — Size of padding
`[0 0]` (default) | vector of two nonnegative integers

`PaddingMode` — Method to determine padding size
`'manual'` (default) | `'same'` | `'causal'`

`PaddingValue` — Value to pad data
`0` (default) | scalar | `"symmetric-include-edge"` | `"symmetric-exclude-edge"` | `"replicate"`

`NumChannels` — Number of input channels
`"auto"` (default) | positive integer

`WeightsInitializer` — Function to initialize weights
`"complex-glorot-normal"` (default) | `"complex-glorot-uniform-square"` | `"complex-he-normal"` | `"complex-he-uniform-square"` | `"complex-narrow-normal"` | `"zeros"` | `"ones"` | function handle

`BiasInitializer` — Function to initialize biases
`"zeros"` (default) | `"complex-narrow-normal"` | `"ones"` | function handle

`Weights` — Layer weights
`[]` (default) | numeric array

`Bias` — Layer biases
`[]` (default) | numeric array

`WeightLearnRateFactor` — Learning rate factor for weights
`1` (default) | nonnegative scalar

`BiasLearnRateFactor` — Learning rate factor for biases
`1` (default) | nonnegative scalar

`WeightL2Factor` — L₂ regularization factor for weights
`1` (default) | nonnegative scalar

`BiasL2Factor` — L₂ regularization factor for biases
`0` (default) | nonnegative scalar

`Name` — Layer name
`''` (default) | character vector

`NumInputs` — Number of inputs
Read-only: `1` (default)

`InputNames` — Input name
Read-only: `{'in'}` (default)

`NumOutputs` — Number of outputs
Read-only: `1` (default)

`OutputNames` — Output name
Read-only: `{'out'}` (default)