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groupNormalizationLayer

Group normalization layer

Since R2020b

Description

A group normalization layer normalizes a mini-batch of data across grouped subsets of channels for each observation independently. To speed up training of the convolutional neural network and reduce the sensitivity to network initialization, use group normalization layers between convolutional layers and nonlinearities, such as ReLU layers.

After normalization, the layer scales the input with a learnable scale factor γ and shifts it by a learnable offset β.

Creation

Description

example

layer = groupNormalizationLayer(numGroups) creates a group normalization layer.

example

layer = groupNormalizationLayer(numGroups,Name,Value) creates a group normalization layer and sets the optional Epsilon, Parameters and Initialization, Learning Rate and Regularization, and Name properties using one or more name-value arguments. You can specify multiple name-value arguments. Enclose each property name in quotes.

Input Arguments

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Number of groups into which to divide the channels of the input data, specified as one of the following:

  • Positive integer – Divide the incoming channels into the specified number of groups. The specified number of groups must divide the number of channels of the input data exactly.

  • 'all-channels' – Group all incoming channels into a single group. This operation is also known as layer normalization. Alternatively, use layerNormalizationLayer.

  • 'channel-wise' – Treat all incoming channels as separate groups. This operation is also known as instance normalization. Alternatively, use instanceNormalizationLayer.

Properties

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Group Normalization

Constant to add to the mini-batch variances, specified as a positive scalar.

The software adds this constant to the mini-batch variances before normalization to ensure numerical stability and avoid division by zero.

Before R2023a: Epsilon must be greater than or equal to 1e-5.

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

This property is read-only.

Number of input channels, specified as one of the following:

  • '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.

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

Parameters and Initialization

Function to initialize the channel scale factors, specified as one of the following:

  • 'ones' – Initialize the channel scale factors with ones.

  • 'zeros' – Initialize the channel scale factors with zeros.

  • 'narrow-normal' – Initialize the channel scale factors by independently sampling from a normal distribution with a mean of zero and standard deviation of 0.01.

  • Function handle – Initialize the channel scale factors with a custom function. If you specify a function handle, then the function must be of the form scale = func(sz), where sz is the size of the scale. For an example, see Specify Custom Weight Initialization Function.

The layer only initializes the channel scale factors when the Scale property is empty.

Data Types: char | string | function_handle

Function to initialize the channel offsets, specified as one of the following:

  • 'zeros' – Initialize the channel offsets with zeros.

  • 'ones' – Initialize the channel offsets with ones.

  • 'narrow-normal' – Initialize the channel offsets by independently sampling from a normal distribution with a mean of zero and standard deviation of 0.01.

  • Function handle – Initialize the channel offsets with a custom function. If you specify a function handle, then the function must be of the form offset = func(sz), where sz is the size of the scale. For an example, see Specify Custom Weight Initialization Function.

The layer only initializes the channel offsets when the Offset property is empty.

Data Types: char | string | function_handle

Channel scale factors γ, specified as a numeric array.

The channel scale factors are learnable parameters. When you train a network using the trainnet function or initialize a dlnetwork object, if Scale is nonempty, then the software uses the Scale property as the initial value. If Scale is empty, then the software uses the initializer specified by ScaleInitializer.

Depending on the type of layer input, the trainnet and dlnetwork functions automatically reshape this property to have of the following sizes:

Layer InputProperty Size
feature inputNumChannels-by-1
vector sequence input

1-D image input (since R2023a)

1-by-NumChannels

1-D image sequence input (since R2023a)

2-D image input1-by-1-by-NumChannels
2-D image sequence input
3-D image input1-by-1-by-1-by-NumChannels
3-D image sequence input

Data Types: single | double

Channel offsets β, specified as a numeric vector.

The channel offsets are learnable parameters. When you train a network using the trainnet function or initialize a dlnetwork object, if Offset is nonempty, then the software uses the Offset property as the initial value. If Offset is empty, then the software uses the initializer specified by OffsetInitializer.

Depending on the type of layer input, the trainnet and dlnetwork functions automatically reshape this property to have of the following sizes:

Layer InputProperty Size
feature inputNumChannels-by-1
vector sequence input

1-D image input (since R2023a)

1-by-NumChannels

1-D image sequence input (since R2023a)

2-D image input1-by-1-by-NumChannels
2-D image sequence input
3-D image input1-by-1-by-1-by-NumChannels
3-D image sequence input

Data Types: single | double

Learning Rate and Regularization

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

The software multiplies this factor by the global learning rate to determine the learning rate for the scale factors in a layer. For example, if ScaleLearnRateFactor is 2, then the learning rate for the scale factors in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings specified with the trainingOptions function.

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

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

The software multiplies this factor by the global learning rate to determine the learning rate for the offsets in a layer. For example, if OffsetLearnRateFactor is 2, then the learning rate for the offsets in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings specified with the trainingOptions function.

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

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

The software multiplies this factor by the global L2 regularization factor to determine the learning rate for the scale factors in a layer. For example, if ScaleL2Factor is 2, then the L2 regularization for the offsets in the layer is twice the global L2 regularization factor. You can specify the global L2 regularization factor using the trainingOptions function.

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

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

The software multiplies this factor by the global L2 regularization factor to determine the learning rate for the offsets in a layer. For example, if OffsetL2Factor is 2, then the L2 regularization for the offsets in the layer is twice the global L2 regularization factor. You can specify the global L2 regularization factor using the trainingOptions function.

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

Layer

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 "".

The GroupNormalizationLayer object stores this property as a character vector.

Data Types: char | string

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

Examples

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Create a group normalization layer that normalizes incoming data across three groups of channels. Name the layer groupnorm.

layer = groupNormalizationLayer(3,Name="groupnorm")
layer = 
  GroupNormalizationLayer with properties:

           Name: 'groupnorm'
    NumChannels: 'auto'

   Hyperparameters
      NumGroups: 3
        Epsilon: 1.0000e-05

   Learnable Parameters
         Offset: []
          Scale: []

Use properties method to see a list of all properties.

Include a group normalization layer in a Layer array. Normalize the incoming 20 channels in four groups.

layers = [
    imageInputLayer([28 28 3])
    convolution2dLayer(5,20)
    groupNormalizationLayer(4)
    reluLayer
    maxPooling2dLayer(2,Stride=2)
    fullyConnectedLayer(10)
    softmaxLayer]
layers = 
  7x1 Layer array with layers:

     1   ''   Image Input           28x28x3 images with 'zerocenter' normalization
     2   ''   2-D Convolution       20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   Group Normalization   Group normalization
     4   ''   ReLU                  ReLU
     5   ''   2-D Max Pooling       2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   ''   Fully Connected       10 fully connected layer
     7   ''   Softmax               softmax

More About

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Algorithms

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References

[1] Wu, Yuxin, and Kaiming He. “Group Normalization.” Preprint submitted June 11, 2018. https://arxiv.org/abs/1803.08494.

Extended Capabilities

C/C++ Code Generation
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Version History

Introduced in R2020b

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