getL2Factor - Get L2 regularization factor of layer

        learnable parameter - MATLAB ([original](https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.layer.getl2factor.html)) ([raw](?raw))

Get L2 regularization factor of layer learnable parameter

Syntax

Description

[factor](#mw%5F7c41dd18-585e-4c4f-8332-5add0d27252a) = getL2Factor([layer](#d126e101638),[parameterName](#d126e101652)) returns the L2 regularization factor of the parameter with the name parameterName inlayer.

For built-in layers, you can get the_L2_ regularization factor directly by using the corresponding property. For example, for aconvolution2dLayer layer, the syntax factor = getL2Factor(layer,'Weights') is equivalent to factor = layer.WeightL2Factor.

example

[factor](#mw%5F7c41dd18-585e-4c4f-8332-5add0d27252a) = getL2Factor([layer](#d126e101638),[parameterPath](#mw%5F2ba61a18-51bd-49f3-b99b-a450e961bf39%5Fsep%5Fmw%5Fbb76b081-6fd1-4a89-9ec7-fd4394ad67fb)) returns the L2 regularization factor of the parameter specified by the path parameterPath. Use this syntax when the layer is a networkLayer or when the parameter is in adlnetwork object in a custom layer.

example

[factor](#mw%5F7c41dd18-585e-4c4f-8332-5add0d27252a) = getL2Factor([net](#mw%5F2ba61a18-51bd-49f3-b99b-a450e961bf39%5Fsep%5Fmw%5F3ffb42b3-6af7-4c22-82b3-4fb0f6a399f2),[layerName](#mw%5F2ba61a18-51bd-49f3-b99b-a450e961bf39%5Fsep%5Fmw%5F5e8103c3-6a84-4c6d-a782-b16533603493),[parameterName](#d126e101652)) returns the L2 regularization factor of the parameter with the name parameterName in the layer with name layerName for the specifieddlnetwork object.

example

[factor](#mw%5F7c41dd18-585e-4c4f-8332-5add0d27252a) = getL2Factor([net](#mw%5F2ba61a18-51bd-49f3-b99b-a450e961bf39%5Fsep%5Fmw%5F3ffb42b3-6af7-4c22-82b3-4fb0f6a399f2),[parameterPath](#mw%5F2ba61a18-51bd-49f3-b99b-a450e961bf39%5Fsep%5Fmw%5Fbb76b081-6fd1-4a89-9ec7-fd4394ad67fb)) returns the L2 regularization factor of the parameter specified by the path parameterPath. Use this syntax when the parameter is in a networkLayer or when the parameter is in adlnetwork object in a custom layer.

example

Examples

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Set and Get L2 Regularization Factor of Learnable Parameter

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

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

Create a layer array including a custom layer sreluLayer.

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

Set the L2 regularization factor of the LeftSlope learnable parameter of the sreluLayer to 2.

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

View the updated L2 regularization factor.

factor = getL2Factor(layers(4),"LeftSlope")

Set and Get L2 Regularization Factor of Custom Nested Layer Learnable Parameter

Set and get the L2 regularization factor of a learnable parameter of a custom nested layer defined using network composition.

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.

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')

Set and Get L2 Regularization Factor of `dlnetwork` Learnable Parameter

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

Specify the layers of the classification branch and add them to the network.

net = dlnetwork; 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')];

net = addLayers(net,layers);

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

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

Get the updated L2 regularization factor using the getL2Factor function.

factor = getL2Factor(net,'conv','Weights')

Set and Get L2 Regularization Factor of Nested Layer Learnable Parameter

Create an array of layers containing an lstmLayer with 100 hidden units and a dropoutLayer with a dropout probability of 0.2.

layers = [lstmLayer(100,OutputMode="sequence",Name="lstm") dropoutLayer(0.2,Name="dropout")];

Create a network layer containing these layers.

lstmDropoutLayer = networkLayer(layers,Name="lstmDropout");

Use the network layer to build a network.

layers = [sequenceInputLayer(3) lstmDropoutLayer lstmDropoutLayer fullyConnectedLayer(10) softmaxLayer];

Create a dlnetwork object. You can also create a dlnetwork object by training the network using the trainnet function.

Set the L2 regularization factor of the InputWeights learnable parameter of the LSTM layer in the first network layer to 2 using the setL2Factor function.

factor = 2; net = setL2Factor(net,"lstmDropout_1/lstm/InputWeights",factor);

Get the updated L2 regularization factor using the getL2Factor function.

factor = getL2Factor(net,"lstmDropout_1/lstm/InputWeights")

Set and Get L2 Regularization Factor of Custom Nested `dlnetwork` Learnable Parameter

Set and get the L2 regularization factor of a learnable parameter of a custom nested layer defined using network composition 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(2numFilters,'Stride',2,'IncludeSkipConvolution',true,'Name','res3') residualBlockLayer(2numFilters,'Name','res4') residualBlockLayer(4numFilters,'Stride',2,'IncludeSkipConvolution',true,'Name','res5') residualBlockLayer(4numFilters,'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')

Input Arguments

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`layer` — Input layer

scalar Layer object

Input layer, specified as a scalar Layer object.

`parameterName` — Parameter name

character vector | string scalar

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

`parameterPath` — Path to parameter in nested layer

string scalar | character vector

Path to parameter in nested layer, specified as a string scalar or a character vector. A nested layer can be a layer within a networkLayer or a custom layer that itself defines a neural network as a learnable parameter.

If the input to getL2Factor is a layer, then:

If the nested layer is in a network layer, the parameter path has the form"nestedLayerName/parameterName" wherenestedlayerName is the name of the nested layer inside the network layer, and parameterName is the name of the parameter. If there are multiple levels of nested layers, then specify the path using the formnestedLayerName1/.../nestedLayerNameN/parameterName.
If the nested layer is a custom layer that itself defines a neural network as a learnable parameter, the parameter path has the form"propertyName/layerName/parameterName" wherepropertyName is the name of the property containing adlnetwork object, layerName is the name of the layer in the dlnetwork object, andparameterName is the name of the parameter. If there are multiple levels of nested layers, then specify the path using the form"propertyName1/layerName1/.../propertyNameN/layerNameN/parameterName".

If the input to getL2Factor is a dlnetwork object and the desired parameter is in a nested layer, then:

If the nested layer is in a network layer, the parameter path has the form"networkLayerName/nestedLayerName/parameterName" wherenetworkLayerName is the name of the network layer,nestedlayerName is the name of the nested layer inside the network layer, and parameterName is the name of the parameter. If there are multiple levels of nested layers, then specify the path using the form"networkLayerName1/.../networkLayerNameN/nestedLayerName/parameterName".
If the nested layer is a custom layer that itself defines a neural network as a learnable parameter, the parameter path has the form"customLayerName1/propertyName/layerName/parameterName", where layerName1 is the name of the layer in the inputdlnetwork object, propertyName is the name of the property of the layer containing a dlnetwork object, layerName is the name of the layer in thedlnetwork object, and parameterName is the name of the parameter. If there are multiple levels of nested layers, then specify the path using the form"customLayerName1/propertyName1/.../customLayerNameN/propertyNameN/layerName/parameterName".

Data Types: char | string

`net` — Neural network

dlnetwork object

Neural network, specified as a dlnetwork object.

`layerName` — Layer name

string scalar | character vector

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

Data Types: char | string

Output Arguments

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`factor` — L2 regularization factor

nonnegative scalar

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 is2, 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

expand all

R2024a: Specify Path to Parameter in Network Layer

Specify the path to a parameter in a networkLayer using the parameterPath argument.

getL2Factor - Get L2 regularization factor of layer

Syntax

Description

Examples

Set and Get L2 Regularization Factor of Learnable Parameter

Set and Get L2 Regularization Factor of Custom Nested Layer Learnable Parameter

Set and Get L2 Regularization Factor of dlnetwork Learnable Parameter

Set and Get L2 Regularization Factor of Nested Layer Learnable Parameter

Set and Get L2 Regularization Factor of Custom Nested dlnetwork Learnable Parameter

Input Arguments

layer — Input layer

parameterName — Parameter name

parameterPath — Path to parameter in nested layer

net — Neural network

layerName — Layer name

Output Arguments

factor — L2 regularization factor

Version History

R2024a: Specify Path to Parameter in Network Layer

Set and Get L2 Regularization Factor of `dlnetwork` Learnable Parameter

Set and Get L2 Regularization Factor of Custom Nested `dlnetwork` Learnable Parameter

`layer` — Input layer

`parameterName` — Parameter name

`parameterPath` — Path to parameter in nested layer

`net` — Neural network

`layerName` — Layer name

`factor` — L2 regularization factor