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getL2Factor

Get L2 regularization factor of layer learnable parameter

Description

example

factor = getL2Factor(layer,parameterName) returns the L2 regularization factor of the parameter with the name parameterName in layer.

For built-in layers, you can get the L2 regularization factor directly by using the corresponding property. For example, for a convolution2dLayer layer, the syntax factor = getL2Factor(layer,'Weights') is equivalent to factor = layer.WeightL2Factor.

example

factor = getL2Factor(layer,parameterPath) returns the L2 regularization factor of the parameter specified by the path parameterPath. Use this syntax when the parameter is in a dlnetwork object in a custom layer.

example

factor = getL2Factor(net,layerName,parameterName) returns the L2 regularization factor of the parameter with the name parameterName in the layer with name layerName for the specified dlnetwork object.

example

factor = getL2Factor(net,parameterPath) returns the L2 regularization factor of the parameter specified by the path parameterPath. Use this syntax when the parameter is in a nested layer.

Examples

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Set and get the L2 regularization factor of a learnable parameter of a layer.

Create a layer array containing the custom layer preluLayer, attached to this is example as a supporting file. To access this layer, open this example as a live script.

Create a layer array including a custom layer preluLayer.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    batchNormalizationLayer
    preluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

Set the L2 regularization factor of the Alpha learnable parameter of the preluLayer to 2.

layers(4) = setL2Factor(layers(4),"Alpha",2);

View the updated L2 regularization factor.

factor = getL2Factor(layers(4),"Alpha")
factor = 2

Set and get the L2 regularization factor of a learnable parameter of a nested layer.

Create a residual block layer using the custom layer residualBlockLayer attached to this example as a supporting file. To access this file, open this example as a Live Script.

numFilters = 64;
layer = residualBlockLayer(numFilters)
layer = 
  residualBlockLayer with properties:

       Name: ''

   Learnable Parameters
    Network: [1x1 dlnetwork]

   State Parameters
    Network: [1x1 dlnetwork]

Use properties method to see a list of all properties.

View the layers of the nested network.

layer.Network.Layers
ans = 
  7x1 Layer array with layers:

     1   'conv_1'        2-D Convolution       64 3x3 convolutions with stride [1  1] and padding 'same'
     2   'batchnorm_1'   Batch Normalization   Batch normalization
     3   'relu_1'        ReLU                  ReLU
     4   'conv_2'        2-D Convolution       64 3x3 convolutions with stride [1  1] and padding 'same'
     5   'batchnorm_2'   Batch Normalization   Batch normalization
     6   'add'           Addition              Element-wise addition of 2 inputs
     7   'relu_2'        ReLU                  ReLU

Set the L2 regularization factor of the learnable parameter 'Weights' of the layer 'conv_1' to 2 using the setL2Factor function.

factor = 2;
layer = setL2Factor(layer,'Network/conv_1/Weights',factor);

Get the updated L2 regularization factor using the getL2Factor function.

factor = getL2Factor(layer,'Network/conv_1/Weights')
factor = 2

Set and get the L2 regularization factor of a learnable parameter of a dlnetwork object.

Create a dlnetwork object.

layers = [
    imageInputLayer([28 28 1],'Normalization','none','Name','in')
    convolution2dLayer(5,20,'Name','conv')
    batchNormalizationLayer('Name','bn')
    reluLayer('Name','relu')
    fullyConnectedLayer(10,'Name','fc')
    softmaxLayer('Name','sm')];

lgraph = layerGraph(layers);

dlnet = dlnetwork(lgraph);

Set the L2 regularization factor of the 'Weights' learnable parameter of the convolution layer to 2 using the setL2Factor function.

factor = 2;
dlnet = setL2Factor(dlnet,'conv','Weights',factor);

Get the updated L2 regularization factor using the getL2Factor function.

factor = getL2Factor(dlnet,'conv','Weights')
factor = 2

Set and get the L2 regularization factor of a learnable parameter of a nested layer in a dlnetwork object.

Create a dlnetwork object containing the custom layer residualBlockLayer attached to this example as a supporting file. To access this file, open this example as a Live Script.

inputSize = [224 224 3];
numFilters = 32;
numClasses = 5;

layers = [
    imageInputLayer(inputSize,'Normalization','none','Name','in')
    convolution2dLayer(7,numFilters,'Stride',2,'Padding','same','Name','conv')
    groupNormalizationLayer('all-channels','Name','gn')
    reluLayer('Name','relu')
    maxPooling2dLayer(3,'Stride',2,'Name','max')
    residualBlockLayer(numFilters,'Name','res1')
    residualBlockLayer(numFilters,'Name','res2')
    residualBlockLayer(2*numFilters,'Stride',2,'IncludeSkipConvolution',true,'Name','res3')
    residualBlockLayer(2*numFilters,'Name','res4')
    residualBlockLayer(4*numFilters,'Stride',2,'IncludeSkipConvolution',true,'Name','res5')
    residualBlockLayer(4*numFilters,'Name','res6')
    globalAveragePooling2dLayer('Name','gap')
    fullyConnectedLayer(numClasses,'Name','fc')
    softmaxLayer('Name','sm')];

dlnet = dlnetwork(layers);

The Learnables property of the dlnetwork object is a table that contains the learnable parameters of the network. The table includes parameters of nested layers in separate rows. View the learnable parameters of the layer "res1".

learnables = dlnet.Learnables;
idx = learnables.Layer == "res1";
learnables(idx,:)
ans=8×3 table
    Layer              Parameter                     Value       
    ______    ____________________________    ___________________

    "res1"    "Network/conv_1/Weights"        {3x3x32x32 dlarray}
    "res1"    "Network/conv_1/Bias"           {1x1x32    dlarray}
    "res1"    "Network/batchnorm_1/Offset"    {1x1x32    dlarray}
    "res1"    "Network/batchnorm_1/Scale"     {1x1x32    dlarray}
    "res1"    "Network/conv_2/Weights"        {3x3x32x32 dlarray}
    "res1"    "Network/conv_2/Bias"           {1x1x32    dlarray}
    "res1"    "Network/batchnorm_2/Offset"    {1x1x32    dlarray}
    "res1"    "Network/batchnorm_2/Scale"     {1x1x32    dlarray}

For the layer "res1", set the L2 regularization factor of the learnable parameter 'Weights' of the layer 'conv_1' to 2 using the setL2Factor function.

factor = 2;
dlnet = setL2Factor(dlnet,'res1/Network/conv_1/Weights',factor);

Get the updated L2 regularization factor using the getL2Factor function.

factor = getL2Factor(dlnet,'res1/Network/conv_1/Weights')
factor = 2

Input Arguments

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Input layer, specified as a scalar Layer object.

Parameter name, specified as a character vector or a string scalar.

Path to parameter in nested layer, specified as a string scalar or a character vector. A nested layer is a custom layer that itself defines a layer graph as a learnable parameter.

If the input to getL2Factor is a nested layer, then the parameter path has the form "propertyName/layerName/parameterName", where:

  • propertyName is the name of the property containing a dlnetwork object

  • layerName is the name of the layer in the dlnetwork object

  • parameterName is the name of the parameter

If there are multiple levels of nested layers, then specify each level using the form "propertyName1/layerName1/.../propertyNameN/layerNameN/parameterName", where propertyName1 and layerName1 correspond to the layer in the input to the getL2Factor function, and the subsequent parts correspond to the deeper levels.

Example: For layer input to getL2Factor, the path "Network/conv1/Weights" specifies the "Weights" parameter of the layer with name "conv1" in the dlnetwork object given by layer.Network.

If the input to getL2Factor is a dlnetwork object and the desired parameter is in a nested layer, then the parameter path has the form "layerName1/propertyName/layerName/parameterName", where:

  • layerName1 is the name of the layer in the input dlnetwork object

  • propertyName is the property of the layer containing a dlnetwork object

  • layerName is the name of the layer in the dlnetwork object

  • parameterName is the name of the parameter

If there are multiple levels of nested layers, then specify each level using the form "layerName1/propertyName1/.../layerNameN/propertyNameN/layerName/parameterName", where layerName1 and propertyName1 correspond to the layer in the input to the getL2Factor function, and the subsequent parts correspond to the deeper levels.

Example: For dlnetwork input to getL2Factor, the path "res1/Network/conv1/Weights" specifies the "Weights" parameter of the layer with name "conv1" in the dlnetwork object given by layer.Network, where layer is the layer with name "res1" in the input network net.

Data Types: char | string

Neural network, specified as a dlnetwork object.

Layer name, specified as a string scalar or a character vector.

Data Types: char | string

Output Arguments

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L2 regularization factor for the parameter, returned as a nonnegative scalar.

The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization for the specified parameter. For example, if factor is 2, then the L2 regularization for the specified parameter is twice the current global L2 regularization. The software determines the global L2 regularization based on the settings specified with the trainingOptions function.

Version History

Introduced in R2017b