layerNormalizationLayer

Layer normalization layer

Since R2021a

expand all in page

    Description

    A layer normalization layer normalizes a mini-batch of data across all channels for each observation independently. To speed up training of recurrent and multilayer perceptron neural networks and reduce the sensitivity to network initialization, use layer normalization layers after the learnable layers, such as LSTM and fully connected layers.

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

    Creation

    Syntax

    layer = layerNormalizationLayer
    layer = layerNormalizationLayer(Name,Value)

    Description

    layer = layerNormalizationLayer creates a layer normalization layer.

    layer = layerNormalizationLayer(Name,Value) sets the optional Epsilon, Parameters and Initialization, Learning Rate and Regularization, and Name properties using one or more name-value arguments. For example, layerNormalizationLayer('Name','layernorm') creates a layer normalization layer with name 'layernorm'.

    example

    Properties

    expand all

    Layer 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

    Since R2023a

    Dimension to normalize over, specified as one of these values:

    • "auto" — For feature, sequence, 1-D image, or spatial-temporal input, normalize over the channel dimension. Otherwise, normalize over the spatial and channel dimensions.

    • "channel-only" — Normalize over the channel dimension.

    • "spatial-channel" — Normalize over the spatial and channel dimensions.

    • "batch-excluded" — Normalize over all dimensions except for the batch dimension.

    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 unnamed layers.

    The LayerNormalizationLayer object stores this property as a character vector.

    Data Types: char | string

    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

    collapse all

    Open Live Script

    Create a layer normalization layer with the name 'layernorm'.

    layer = layerNormalizationLayer('Name','layernorm')
    layer = 
  LayerNormalizationLayer with properties:

                  Name: 'layernorm'
           NumChannels: 'auto'

   Hyperparameters
               Epsilon: 1.0000e-05
    OperationDimension: 'auto'

   Learnable Parameters
                Offset: []
                 Scale: []

  Show all properties

    Include a layer normalization layer in a Layer array.

    layers = [
    imageInputLayer([32 32 3]) 
    convolution2dLayer(3,16,'Padding',1)
    layerNormalizationLayer
    reluLayer   
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding',1)
    layerNormalizationLayer
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer]
    layers = 
  10×1 Layer array with layers:

     1   ''   Image Input           32×32×3 images with 'zerocenter' normalization
     2   ''   2-D Convolution       16 3×3 convolutions with stride [1  1] and padding [1  1  1  1]
     3   ''   Layer Normalization   Layer normalization
     4   ''   ReLU                  ReLU
     5   ''   2-D Max Pooling       2×2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   ''   2-D Convolution       32 3×3 convolutions with stride [1  1] and padding [1  1  1  1]
     7   ''   Layer Normalization   Layer normalization
     8   ''   ReLU                  ReLU
     9   ''   Fully Connected       10 fully connected layer
    10   ''   Softmax               softmax

    Algorithms

    expand all

    References

    [1] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. “Layer Normalization.” Preprint, submitted July 21, 2016. https://arxiv.org/abs/1607.06450.

    Extended Capabilities

    expand all

    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 R2021a

    expand all

    See Also

    | | | | | | | |

    Topics