zreluLayer

ZReLU Layer

Since R2026a

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    Description

    A zReLU layer performs a threshold operation to complex inputs, where any value with negative real or imaginary part is set to zero.

    Creation

    Syntax

    layer = zreluLayer
    layer = zreluLayer(Name=name)

    Description

    layer = zreluLayer creates a zReLU layer.

    layer = zreluLayer(Name=name) creates a zReLU layer and sets the optional Name property using a name-value pair.

    example

    Input Arguments

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    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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    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

    This property is read-only.

    Number of inputs to the layer, stored as 1. This layer accepts a single input only.

    Data Types: double

    This property is read-only.

    Input names, stored as {'in'}. This layer accepts a single input only.

    Data Types: cell

    This property is read-only.

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

    Data Types: double

    This property is read-only.

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

    Data Types: cell

    Examples

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    Open Live Script

    Create a zReLU layer with the name 'zrelu1'.

    layer = zreluLayer(Name="zrelu1")
    layer = 
  ZReLULayer with properties:

    Name: 'zrelu1'
    Open Live Script

    Create a layer array including a sequence input layer and two complex fully connected layers separated by a zReLU layer.

    layers = [...
    sequenceInputLayer(100), ...
    complexFullyConnectedLayer(10), ...
    zreluLayer, ...
    complexFullyConnectedLayer(1), ...
    ];

    Convert the layer array to a dlnetwork object.

    net = dlnetwork(layers);

    Create random sample complex sequence data.

    data = randn(1,100) + 1i * randn(1,100);

    Compute the output of the untrained network.

    predict(net,data)
    ans = single

-0.2372 + 0.5038i

    More About

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    Version History

    Introduced in R2026a

    See Also

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