rmspropupdate

Update parameters using root mean squared propagation (RMSProp)

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Syntax

[dlnet,averageSqGrad] = rmspropupdate(dlnet,grad,averageSqGrad)
[params,averageSqGrad] = rmspropupdate(params,grad,averageSqGrad)
[___] = rmspropupdate(___learnRate,sqGradDecay,epsilon)

Description

Update the network learnable parameters in a custom training loop using the root mean squared propagation (RMSProp) algorithm.

Note

This function applies the RMSProp optimization algorithm to update network parameters in custom training loops that use networks defined as dlnetwork objects or model functions. If you want to train a network defined as a Layer array or as a LayerGraph, use the following functions:

example

[dlnet,averageSqGrad] = rmspropupdate(dlnet,grad,averageSqGrad) updates the learnable parameters of the network dlnet using the RMSProp algorithm. Use this syntax in a training loop to iteratively update a network defined as a dlnetwork object.

example

[params,averageSqGrad] = rmspropupdate(params,grad,averageSqGrad) updates the learnable parameters in params using the RMSProp algorithm. Use this syntax in a training loop to iteratively update the learnable parameters of a network defined using functions.

example

[___] = rmspropupdate(___learnRate,sqGradDecay,epsilon) also specifies values to use for the global learning rate, square gradient decay, and small constant epsilon, in addition to the input arguments in previous syntaxes.

Examples

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Perform a single root mean squared propagation update step with a global learning rate of 0.05 and squared gradient decay factor of 0.95.

Create the parameters and parameter gradients as numeric arrays. 

params = rand(3,3,4);
grad = ones(3,3,4);

Initialize the average squared gradient for the first iteration.

averageSqGrad = [];

Specify custom values for the global learning rate and squared gradient decay factor.

learnRate = 0.05;
sqGradDecay = 0.95;

Update the learnable parameters using rmspropupdate.

[params,averageSqGrad] = rmspropupdate(params,grad,averageSqGrad,learnRate,sqGradDecay);
Open Live Script

Use rmspropupdate to train a network using the root mean squared propagation (RMSProp) algorithm.

Load Training Data

Load the digits training data.

[XTrain,YTrain] = digitTrain4DArrayData;
classes = categories(YTrain);
numClasses = numel(classes);

Define the Network

Define the network architecture and specify the average image value using the 'Mean' option in the image input layer.

layers = [
    imageInputLayer([28 28 1], 'Name','input','Mean',mean(XTrain,4))
    convolution2dLayer(5,20,'Name','conv1')
    reluLayer('Name', 'relu1')
    convolution2dLayer(3,20,'Padding',1,'Name','conv2')
    reluLayer('Name','relu2')
    convolution2dLayer(3,20,'Padding',1,'Name','conv3')
    reluLayer('Name','relu3')
    fullyConnectedLayer(numClasses,'Name','fc')
    softmaxLayer('Name','softmax')];
lgraph = layerGraph(layers);

Create a dlnetwork object from the layer graph.

dlnet = dlnetwork(lgraph);

Define Model Gradients Function

Create the helper function modelGradients, listed at the end of the example. The function takes a dlnetwork object dlnet and a mini-batch of input data dlX with corresponding labels Y, and returns the loss and the gradients of the loss with respect to the learnable parameters in dlnet.

Specify Training Options

Specify the options to use during training.

miniBatchSize = 128;
numEpochs = 20;
numObservations = numel(YTrain);
numIterationsPerEpoch = floor(numObservations./miniBatchSize);

Train on a GPU, if one is available. Using a GPU requires Parallel Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher.

executionEnvironment = "auto";

Visualize the training progress in a plot.

plots = "training-progress";

Train Network

Train the model using a custom training loop. For each epoch, shuffle the data and loop over mini-batches of data. Update the network parameters using the rmspropupdate function. At the end of each epoch, display the training progress.

Initialize the training progress plot.

if plots == "training-progress"
    figure
    lineLossTrain = animatedline('Color',[0.85 0.325 0.098]);
    ylim([0 inf])
    xlabel("Iteration")
    ylabel("Loss")
    grid on
end

Initialize the squared average gradients. 

averageSqGrad = [];

Train the network.

iteration = 0;
start = tic;

for epoch = 1:numEpochs
    % Shuffle data.
    idx = randperm(numel(YTrain));
    XTrain = XTrain(:,:,:,idx);
    YTrain = YTrain(idx);
    
    for i = 1:numIterationsPerEpoch
        iteration = iteration + 1;
        
        % Read mini-batch of data and convert the labels to dummy
        % variables.
        idx = (i-1)*miniBatchSize+1:i*miniBatchSize;
        X = XTrain(:,:,:,idx);
        
        Y = zeros(numClasses, miniBatchSize, 'single');
        for c = 1:numClasses
            Y(c,YTrain(idx)==classes(c)) = 1;
        end
        
        % Convert mini-batch of data to a dlarray.
        dlX = dlarray(single(X),'SSCB');
        
        % If training on a GPU, then convert data to a gpuArray.
        if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
            dlX = gpuArray(dlX);
        end
        
        % Evaluate the model gradients and loss using dlfeval and the
        % modelGradients helper function.
        [gradients,loss] = dlfeval(@modelGradients,dlnet,dlX,Y);
        
        % Update the network parameters using the RMSProp optimizer.
        [dlnet,averageSqGrad] = rmspropupdate(dlnet,gradients,averageSqGrad);
        
        % Display the training progress.
        if plots == "training-progress"
            D = duration(0,0,toc(start),'Format','hh:mm:ss');
            addpoints(lineLossTrain,iteration,double(gather(extractdata(loss))))
            title("Epoch: " + epoch + ", Elapsed: " + string(D))
            drawnow
        end
    end
end

Test the Network

Test the classification accuracy of the model by comparing the predictions on a test set with the true labels.

[XTest, YTest] = digitTest4DArrayData;

Convert the data to a dlarray with dimension format 'SSCB'. For GPU prediction, also convert the data to a gpuArray.

dlXTest = dlarray(XTest,'SSCB');
if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
    dlXTest = gpuArray(dlXTest);
end

To classify images using a dlnetwork object, use the predict function and find the classes with the highest scores.

dlYPred = predict(dlnet,dlXTest);
[~,idx] = max(extractdata(dlYPred),[],1);
YPred = classes(idx);

Evaluate the classification accuracy.

accuracy = mean(YPred==YTest)
accuracy = 0.9860

Model Gradients Function

The helper function modelGradients takes a dlnetwork object dlnet and a mini-batch of input data dlX with corresponding labels Y, and returns the loss and the gradients of the loss with respect to the learnable parameters in dlnet. To compute the gradients automatically, use the dlgradient function.

function [gradients,loss] = modelGradients(dlnet,dlX,Y)

dlYPred = forward(dlnet,dlX);

loss = crossentropy(dlYPred,Y);

gradients = dlgradient(loss,dlnet.Learnables);

end

Input Arguments

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Network, specified as a dlnetwork object.

The function updates the dlnet.Learnables property of the dlnetwork object. dlnet.Learnables is a table with three variables:

  • Layer — Layer name, specified as a string scalar.

  • Parameter — Parameter name, specified as a string scalar.

  • Value — Value of parameter, specified as a cell array containing a dlarray.

The input argument grad must be a table of the same form as dlnet.Learnables.

Network learnable parameters, specified as a dlarray, a numeric array, a cell array, a structure, or a table.

If you specify params as a table, it must contain the following three variables.

  • Layer — Layer name, specified as a string scalar.

  • Parameter — Parameter name, specified as a string scalar.

  • Value — Value of parameter, specified as a cell array containing a dlarray.

You can specify params as a container of learnable parameters for your network using a cell array, structure, or table, or nested cell arrays or structures. The learnable parameters inside the cell array, structure, or table must be dlarray or numeric values of data type double or single.

The input argument grad must be provided with exactly the same data type, ordering, and fields (for structures) or variables (for tables) as params.

Data Types: single | double | struct | table | cell

Gradients of the loss, specified as a dlarray, a numeric array, a cell array, a structure, or a table.

The exact form of grad depends on the input network or learnable parameters. The following table shows the required format for grad for possible inputs to rmspropupdate.

InputLearnable ParametersGradients
dlnetTable dlnet.Learnables containing Layer, Parameter, and Value variables. The Value variable consists of cell arrays that contain each learnable parameter as a dlarray. Table with the same data type, variables, and ordering as dlnet.Learnables. grad must have a Value variable consisting of cell arrays that contain the gradient of each learnable parameter.
paramsdlarraydlarray with the same data type and ordering as params
Numeric arrayNumeric array with the same data type and ordering as params
Cell arrayCell array with the same data types, structure, and ordering as params
StructureStructure with the same data types, fields, and ordering as params
Table with Layer, Parameter, and Value variables. The Value variable must consist of cell arrays that contain each learnable parameter as a dlarray.Table with the same data types, variables, and ordering as params. grad must have a Value variable consisting of cell arrays that contain the gradient of each learnable parameter.

You can obtain grad from a call to dlfeval that evaluates a function that contains a call to dlgradient. For more information, see Use Automatic Differentiation In Deep Learning Toolbox.

Moving average of squared parameter gradients, specified as an empty array, a dlarray, a numeric array, a cell array, a structure, or a table.

The exact form of averageSqGrad depends on the input network or learnable parameters. The following table shows the required format for averageSqGrad for possible inputs to rmspropupdate.

InputLearnable ParametersAverage Squared Gradients
dlnetTable dlnet.Learnables containing Layer, Parameter, and Value variables. The Value variable consists of cell arrays that contain each learnable parameter as a dlarray. Table with the same data type, variables, and ordering as dlnet.Learnables. averageSqGrad must have a Value variable consisting of cell arrays that contain the average squared gradient of each learnable parameter.
paramsdlarraydlarray with the same data type and ordering as params
Numeric arrayNumeric array with the same data type and ordering as params
Cell arrayCell array with the same data types, structure, and ordering as params
StructureStructure with the same data types, fields, and ordering as params
Table with Layer, Parameter, and Value variables. The Value variable must consist of cell arrays that contain each learnable parameter as a dlarray.Table with the same data types, variables, and ordering as params. averageSqGrad must have a Value variable consisting of cell arrays that contain the average squared gradient of each learnable parameter.

If you specify averageSqGrad as an empty array, the function assumes no previous gradients and runs in the same way as for the first update in a series of iterations. To update the learnable parameters iteratively, use the averageSqGrad output of a previous call to rmspropupdate as the averageSqGrad input.

Global learning rate, specified as a positive scalar. The default value of learnRate is 0.001.

If you specify the network parameters as a dlnetwork, the learning rate for each parameter is the global learning rate multiplied by the corresponding learning rate factor property defined in the network layers.

Squared gradient decay factor, specified as a positive scalar between 0 and 1. The default value of sqGradDecay is 0.9.

Small constant for preventing divide-by-zero errors, specified as a positive scalar. The default value of epsilon is 1e-8.

Output Arguments

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Network, returned as a dlnetwork object.

The function updates the dlnet.Learnables property of the dlnetwork object.

Updated network learnable parameters, returned as a dlarray, a numeric array, a cell array, a structure, or a table with a Value variable containing the updated learnable parameters of the network.

Updated moving average of squared parameter gradients, returned as a dlarray, a numeric array, a cell array, a structure, or a table.

More About

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RMSProp

The function uses the root mean squared propagation algorithm to update the learnable parameters. For more information, see the definition of the RMSProp algorithm under Stochastic Gradient Descent on the trainingOptions reference page.

Compatibility Considerations

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Behavior changed in R2020a

Extended Capabilities

See Also

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Topics

Introduced in R2019b

Deep Learning Toolbox Documentation

Support