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setLearnRateFactor

Set learn rate factor of layer learnable parameter

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Syntax

layerUpdated = setLearnRateFactor(layer,parameterName,factor)
layerUpdated = setLearnRateFactor(layer,parameterPath,factor)
netUpdated = setLearnRateFactor(net,layerName,parameterName,factor)
netUpdated = setLearnRateFactor(net,parameterPath,factor)

Description

example

layerUpdated = setLearnRateFactor(layer,parameterName,factor) sets the learn rate factor of the parameter with the name parameterName in layer to factor.

For built-in layers, you can set the learn rate factor directly by using the corresponding property. For example, for a convolution2dLayer layer, the syntax layer = setLearnRateFactor(layer,'Weights',factor) is equivalent to layer.WeightLearnRateFactor = factor.

example

layerUpdated = setLearnRateFactor(layer,parameterPath,factor) sets the learn rate 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

netUpdated = setLearnRateFactor(net,layerName,parameterName,factor) sets the learn rate factor of the parameter with the name parameterName in the layer with name layerName for the specified dlnetwork object.

example

netUpdated = setLearnRateFactor(net,parameterPath,factor) sets the learn rate 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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Open Live Script

Set and get the learning rate factor of a learnable parameter of a custom PReLU 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.

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

Set the learn rate factor of the Alpha learnable parameter of the preluLayer to 2.

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

View the updated learn rate factor.

factor = getLearnRateFactor(layers(4),"Alpha")
factor = 2
Open Live Script

Set and get the learning rate 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 learning rate factor of the learnable parameter 'Weights' of the layer 'conv_1' to 2 using the setLearnRateFactor function.

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

Get the updated learning rate factor using the getLearnRateFactor function.

factor = getLearnRateFactor(layer,'Network/conv_1/Weights')
factor = 2
Open Live Script

Set and get the learning rate 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 learn rate factor of the 'Weights' learnable parameter of the convolution layer to 2 using the setLearnRateFactor function.

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

Get the updated learn rate factor using the getLearnRateFactor function.

factor = getLearnRateFactor(dlnet,'conv','Weights')
factor = 2
Open Live Script

Set and get the learning rate 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);

View the layers of the nested network in the layer 'res1'.

dlnet.Layers(6).Network.Layers
ans = 
  7x1 Layer array with layers:

     1   'conv_1'        2-D Convolution       32 3x3x32 convolutions with stride [1  1] and padding 'same'
     2   'batchnorm_1'   Batch Normalization   Batch normalization with 32 channels
     3   'relu_1'        ReLU                  ReLU
     4   'conv_2'        2-D Convolution       32 3x3x32 convolutions with stride [1  1] and padding 'same'
     5   'batchnorm_2'   Batch Normalization   Batch normalization with 32 channels
     6   'add'           Addition              Element-wise addition of 2 inputs
     7   'relu_2'        ReLU                  ReLU

Set the learning rate factor of the learnable parameter 'Weights' of the layer 'conv_1' to 2 using the setLearnRateFactor function.

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

Get the updated learning rate factor using the getLearnRateFactor function.

factor = getLearnRateFactor(dlnet,'res1/Network/conv_1/Weights')
factor = 2
Open Live Script

Load a pretrained network.

net = squeezenet;

Convert the network to a layer graph, remove the output layer, and convert it to a dlnetwork object.

lgraph = layerGraph(net);
lgraph = removeLayers(lgraph,'ClassificationLayer_predictions');
dlnet = dlnetwork(lgraph);

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 first few rows of the learnables table.

learnables = dlnet.Learnables;
head(learnables)
          Layer           Parameter           Value       
    __________________    _________    ___________________

    "conv1"               "Weights"    {3x3x3x64  dlarray}
    "conv1"               "Bias"       {1x1x64    dlarray}
    "fire2-squeeze1x1"    "Weights"    {1x1x64x16 dlarray}
    "fire2-squeeze1x1"    "Bias"       {1x1x16    dlarray}
    "fire2-expand1x1"     "Weights"    {1x1x16x64 dlarray}
    "fire2-expand1x1"     "Bias"       {1x1x64    dlarray}
    "fire2-expand3x3"     "Weights"    {3x3x16x64 dlarray}
    "fire2-expand3x3"     "Bias"       {1x1x64    dlarray}

To freeze the learnable parameters of the network, loop over the learnable parameters and set the learn rate to 0 using the setLearnRateFactor function.

factor = 0;

numLearnables = size(learnables,1);
for i = 1:numLearnables
    layerName = learnables.Layer(i);
    parameterName = learnables.Parameter(i);
    
    dlnet = setLearnRateFactor(dlnet,layerName,parameterName,factor);
end

To use the updated learn rate factors when training, you must pass the dlnetwork object to the update function in the custom training loop. For example, use the command

[dlnet,velocity] = sgdmupdate(dlnet,gradients,velocity);

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.

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

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

Example: 2

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 setLearnRateFactor 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 setLearnRateFactor function, and the subsequent parts correspond to the deeper levels.

Example: For layer input to setLearnRateFactor, 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 setLearnRateFactor 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 setLearnRateFactor function, and the subsequent parts correspond to the deeper levels.

Example: For dlnetwork input to setLearnRateFactor, 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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Updated layer, returned as a Layer.

Updated network, returned as a dlnetwork.

Version History

Introduced in R2017b

See Also

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