scalingLayer

Scaling layer

Description

A scaling layer linearly scales and offsets the input data.

Creation

Syntax

layer = scalingLayer

layer = scalingLayer(Name=Value)

Description

layer = scalingLayer creates a scaling layer.

layer = scalingLayer(Name=Value) specifies options using one or more name-value arguments. For example, Scale=0.5 specifies to scale the input by 0.5.

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.

Example: scalingLayer(Scale=0.1,Offset=0.4) creates a scaling layer that scales and offsets the input using values of 0.1 and 0.4, respectively.

Scale — Scaling factor

1 (default) | numeric scalar | numeric array

Scaling factor, specified as one of these values:

• Numeric scalar — Scale all input values using the specified value.

• Numeric array — Scale each element of the input using the specified array with implicit expansion.

Scale and Offset must have sizes such that the size of Scale.*X + Offset matches the size of X, where X is the layer input. (since R2025a)

Before R2025a: If Scale and Offset are both numeric arrays, then they must be the same size.

This argument sets the Scale property.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Offset — Offset

0 (default) | numeric scalar | numeric array

Since R2025a

Offset, specified as one of these values:

• Numeric scalar — Offset all scaled input values using the specified value.

• Numeric array — Offset each element of the scaled input using the specified array with implicit expansion.

Scale and Offset must have sizes such that the size of Scale.*X + Offset matches the size of X, where X is the layer input. (since R2025a)

Before R2025a: If Scale and Offset are both numeric arrays, then they must be the same size.

Before R2025a: Use Bias instead.

This argument sets the Offset property.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name — Layer name

"scaling" (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

Scaling

Scale — Scaling factor

1 (default) | numeric scalar | numeric array

Scaling factor, specified as one of these values:

• Numeric scalar — Scale all input values using the specified value.

• Numeric array — Scale each element of the input using the specified array with implicit expansion.

Scale and Offset must have sizes such that the size of Scale.*X + Offset matches the size of X, where X is the layer input. (since R2025a)

Before R2025a: If Scale and Offset are both numeric arrays, then they must be the same size.

The ScalingLayer object stores this property as double type.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Offset — Offset

0 (default) | numeric scalar | numeric array

Since R2025a

Offset, specified as one of these values:

• Numeric scalar — Offset all scaled input values using the specified value.

• Numeric array — Offset each element of the scaled input using the specified array with implicit expansion.

Scale and Offset must have sizes such that the size of Scale.*X + Offset matches the size of X, where X is the layer input. (since R2025a)

Before R2025a: If Scale and Offset are both numeric arrays, then they must be the same size.

Before R2025a: Use Bias instead.

The ScalingLayer object stores this property as double type.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Layer

Name — Layer name

'scaling' (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 ScalingLayer object stores this property as a character vector.

Data Types: char | string

Description — One-line description of the layer

Read-only: 'Scaling' (default)

This property is read-only.

One-line description of the layer, stored as 'Scaling'. This description appears when you display a Layer array and when you use the analyzeNetwork function.

Type — Type of the layer

Read-only: 'Scaling' (default)

This property is read-only.

Type of the layer, stored as 'Scaling'. The type appears when you display a Layer array and when you use the analyzeNetwork function.

Data Types: char

NumInputs — Number of inputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

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

This property is read-only.

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

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 Scaling Layer

Create a scaling layer that scales the input with a factor of 0.1 and adds an offset of 0.4.

layer = scalingLayer(Scale=0.1,Offset=0.4);

Include a scaling layer in a layer array.

layers = [
    featureInputLayer(1)
    fullyConnectedLayer(400)
    reluLayer
    fullyConnectedLayer(300)
    reluLayer
    fullyConnectedLayer(1)
    tanhLayer
    scalingLayer(Scale=0.1,Offset=0.4)];

Specify Scale and Offsets for Each Input Element

To scale and offset each element of the input using different values, specify the scale and offset using numeric arrays.

Create a scaling layer that scales and offsets the input using the scaling and offset values [1 2 3]' and [0.1 0.2 0.3]', respectively.

layer = scalingLayer(Scale=[1 2 3]',Bias=[0.1 0.2 0.3]');

Include a scaling layer with the same scale and offset in a layer array. In this layer array, the output size of the fully connected layer is 3 (and in turn, the subsequent tanh layer), so the scaling layer receives a 3-by-$\mathit{N}$ array, where $$N$$ is the batch size. The scaling layer transforms each 3-by-1 vector in its input using implicit expansion.

layers = [
    featureInputLayer(1)
    fullyConnectedLayer(400)
    reluLayer
    fullyConnectedLayer(300)
    reluLayer
    fullyConnectedLayer(3)
    tanhLayer
    scalingLayer(Scale=[1 2 3]',Bias=[0.1 0.2 0.3]')];

Algorithms

Scaling Layer

A scaling layer linearly scales and offsets the input data.

The scaling and offset operation is given by Y = Scale.*X + Offset, where X is the layer input. The scaling operation supports implicit expansion. That is, the layer automatically adjusts the dimensions Scale and Offset so that it has the same size as the input during the layer operation.

This means you can simplify your code by using arrays of smaller sizes for Scale and Offset, or create a layer that processes data independently over different dimensions.

Scale and Offset must have sizes such that the size of Scale.*X + Offset matches the size of X, where X is the layer input. (since R2025a)

Before R2025a: If Scale and Offset are both numeric arrays, then they must be the same size.

Layer Input and 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 describe 2-D image data that is represented as a 4-D array, where the first two dimensions correspond to the spatial dimensions of the images, the third dimension corresponds to the channels of the images, and the fourth dimension corresponds to the batch dimension, as having the format "SSCB" (spatial, spatial, channel, batch).

ScalingLayer objects apply an element-wise operation and support input data of any format. The layer does not add or remove any dimensions, so it outputs data with the same format as its input data.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Version History

Introduced in R2019a

R2025a: Moved to Deep Learning Toolbox

This layer now requires Deep Learning Toolbox™ only. In previous releases, this layer also required Reinforcement Learning Toolbox™.

There are some changes to the layer:

• The Bias property is not recommended. Use the Offset property instead. Code that uses the Bias property continues to work.

• The default value of the Description property is 'Scaling'. In previous releases, the default value is 'Scaling layer'.

• Setting the Description using a name-value argument is not recommended. Use the default value instead. Code that sets the Description argument continues to work.

• The default value of the Type property is 'Scaling'. In previous releases, the default value is "ScalingLayer".

• scalingLayer objects have different class names. If you have code that refers to the previous class names of these objects, then replace the references with the new class names. In most cases, your code will continue to run.

  This table shows the change of outputs of the class function for this layers.

  layer	class(layer) (Before R2025a)	class(layer) (Starting in R2025a)
  scalingLayer object	'rl.layer.ScalingLayer'	'nnet.cnn.layer.ScalingLayer'

  The isa function returns the same value as for previous releases. For example, in R2025a, both isa(layer,"nnet.cnn.layer.ScalingLayer") and isa(layer,"rl.layer.ScalingLayer") return 1 (true) for scalingLayer objects.

  If you have code that uses the value returned by the class function, then you must update your code to use the new class name. For example, replace instances of

  if class(layer) == "rl.layer.ScalingLayer"
      ...
  end

  with

  if isa(layer,"nnet.cnn.layer.ScalingLayer")
      ...
  end