Train Network Using Model Function

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This example shows how to create and train a deep learning network by using functions rather than a layer graph or a dlnetwork. The advantage of using functions is the flexibility to describe a wide variety of networks. The disadvantage is that you must complete more steps and prepare your data carefully. This example uses images of handwritten digits, with the dual objectives of classifying the digits and determining the angle of each digit from the vertical.

Load Training Data

The digitTrain4DArrayData function loads the images, their digit labels, and their angles of rotation from the vertical.

[XTrain,YTrain,anglesTrain] = digitTrain4DArrayData;
classNames = categories(YTrain);
numClasses = numel(classNames);
numObservations = numel(YTrain);

View some images from the training data.

idx = randperm(numObservations,64);
I = imtile(XTrain(:,:,:,idx));
figure
imshow(I)

Define Deep Learning Model

Define the following network that predicts both labels and angles of rotation.

  • A convolution-batchnorm-ReLU block with 16 5-by-5 filters.

  • A branch of two convolution-batchnorm blocks each with 32 3-by-3 filters with a ReLU operation between

  • A skip connection with a convolution-batchnorm block with 32 1-by-1 convolutions.

  • Combine both branches using addition followed by a ReLU operation

  • For the regression output, a branch with a fully connected operation of size 1 (the number of responses).

  • For classification output, a branch with a fully connected operation of size 10 (the number of classes) and a softmax operation.

Define and Initialize Model Parameters and State

Define the parameters for each of the operations and include them in a struct. Use the format parameters.OperationName.ParameterName where parameters is the struct, OperationName is the name of the operation (for example "conv_1") and ParameterName is the name of the parameter (for example, "Weights").

Create a struct parameters containing the model parameters. Initialize the learnable layer weights using the example function initializeGaussian, listed at the end of the example. Initialize the learnable layer biases with zeros. Initialize the batch normalization offset and scale parameters with zeros and ones, respectively.

To perform training and inference using batch normalization layers, you must also manage the network state. Before prediction, you must specify the dataset mean and variance derived from the training data. Create a struct state containing the state parameters. Initialize the batch normalization trained mean and trained variance states with zeros and ones, respectively.

parameters.conv1.Weights = dlarray(initializeGaussian([5,5,1,16]));
parameters.conv1.Bias = dlarray(zeros(16,1,'single'));

parameters.batchnorm1.Offset = dlarray(zeros(16,1,'single'));
parameters.batchnorm1.Scale = dlarray(ones(16,1,'single'));
state.batchnorm1.TrainedMean = zeros(16,1,'single');
state.batchnorm1.TrainedVariance  = ones(16,1,'single');

parameters.convSkip.Weights = dlarray(initializeGaussian([1,1,16,32]));
parameters.convSkip.Bias = dlarray(zeros(32,1,'single'));

parameters.batchnormSkip.Offset = dlarray(zeros(32,1,'single'));
parameters.batchnormSkip.Scale = dlarray(ones(32,1,'single'));
state.batchnormSkip.TrainedMean = zeros(32,1,'single');
state.batchnormSkip.TrainedVariance = ones(32,1,'single');

parameters.conv2.Weights = dlarray(initializeGaussian([3,3,16,32]));
parameters.conv2.Bias = dlarray(zeros(32,1,'single'));

parameters.batchnorm2.Offset = dlarray(zeros(32,1,'single'));
parameters.batchnorm2.Scale = dlarray(ones(32,1,'single'));
state.batchnorm2.TrainedMean = zeros(32,1,'single');
state.batchnorm2.TrainedVariance  = ones(32,1,'single');

parameters.conv3.Weights = dlarray(initializeGaussian([3,3,32,32]));
parameters.conv3.Bias = dlarray(zeros(32,1,'single'));

parameters.batchnorm3.Offset = dlarray(zeros(32,1,'single'));
parameters.batchnorm3.Scale = dlarray(ones(32,1,'single'));
state.batchnorm3.TrainedMean = zeros(32,1,'single');
state.batchnorm3.TrainedVariance  = ones(32,1,'single');

parameters.fc2.Weights = dlarray(initializeGaussian([10,6272]));
parameters.fc2.Bias = dlarray(zeros(numClasses,1,'single'));

parameters.fc1.Weights = dlarray(initializeGaussian([1,6272]));
parameters.fc1.Bias = dlarray(zeros(1,1,'single'));

View the struct of the parameters.

parameters
parameters = struct with fields:
            conv1: [1×1 struct]
       batchnorm1: [1×1 struct]
         convSkip: [1×1 struct]
    batchnormSkip: [1×1 struct]
            conv2: [1×1 struct]
       batchnorm2: [1×1 struct]
            conv3: [1×1 struct]
       batchnorm3: [1×1 struct]
              fc2: [1×1 struct]
              fc1: [1×1 struct]

View the parameters for the "conv1" operation.

parameters.conv1
ans = struct with fields:
    Weights: [5×5×1×16 dlarray]
       Bias: [16×1 dlarray]

View the struct of the state.

state
state = struct with fields:
       batchnorm1: [1×1 struct]
    batchnormSkip: [1×1 struct]
       batchnorm2: [1×1 struct]
       batchnorm3: [1×1 struct]

View the state parameters for the "batchnorm1" operation.

state.batchnorm1
ans = struct with fields:
        TrainedMean: [16×1 single]
    TrainedVariance: [16×1 single]

Define Model Function

Create the function model, listed at the end of the example, that computes the outputs of the deep learning model described earlier.

The function model takes the input data dlX, the model parameters parameters, the flag doTraining which specifies whether to model should return outputs for training or prediction, and the network state state. The network outputs the predictions for the labels, the predictions for the angles, and the updated network state.

Define Model Gradients Function

Create the function modelGradients, listed at the end of the example, that takes a mini-batch of input data dlX with corresponding targets T1 and T2 containing the labels and angles, respectively, and returns the gradients of the loss with respect to the learnable parameters, the updated network state, and the corresponding loss.

Specify Training Options

Specify the training options.

numEpochs = 20;
miniBatchSize = 128;
plots = "training-progress";

numIterationsPerEpoch = floor(numObservations./miniBatchSize);

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

executionEnvironment = "auto";

Train Model

Train the model using a custom training loop.

For each epoch, shuffle the data and loop over mini-batches of data. At the end of each epoch, display the training progress.

For each mini-batch:

  • Convert the labels to dummy variables.

  • Convert the data to dlarray objects with underlying type single and specify the dimension labels 'SSCB' (spatial, spatial, channel, batch).

  • For GPU training, convert to gpuArray objects.

  • Evaluate the model gradients and loss using dlfeval and the modelGradients function.

  • Update the network parameters using the adamupdate function.

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 parameters for Adam.

trailingAvg = [];
trailingAvgSq = [];

Train the model.

iteration = 0;
start = tic;

% Loop over epochs.
for epoch = 1:numEpochs
    
    % Shuffle data.
    idx = randperm(numObservations);
    XTrain = XTrain(:,:,:,idx);
    YTrain = YTrain(idx);
    anglesTrain = anglesTrain(idx);
    
    % Loop over mini-batches
    for i = 1:numIterationsPerEpoch
        iteration = iteration + 1;
        idx = (i-1)*miniBatchSize+1:i*miniBatchSize;
        
        % Read mini-batch of data and convert the labels to dummy
        % variables.
        X = XTrain(:,:,:,idx);
        
        Y1 = zeros(numClasses, miniBatchSize, 'single');
        for c = 1:numClasses
            Y1(c,YTrain(idx)==classNames(c)) = 1;
        end
        
        Y2 = anglesTrain(idx)';
        Y2 = single(Y2);
        
        % Convert mini-batch of data to dlarray.
        dlX = dlarray(X,'SSCB');
        
        % If training on a GPU, then convert data to gpuArray.
        if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
            dlX = gpuArray(dlX);
        end
        
        % Evaluate the model gradients, state, and loss using dlfeval and the
        % modelGradients function.
        [gradients,state,loss] = dlfeval(@modelGradients, dlX, Y1, Y2, parameters, state);
        
        % Update the network parameters using the Adam optimizer.
        [parameters,trailingAvg,trailingAvgSq] = adamupdate(parameters,gradients, ...
            trailingAvg,trailingAvgSq,iteration);
        
        % 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 Model

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

[XTest,YTest,anglesTest] = digitTest4DArrayData;

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

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

To predict the labels and angles of the validation data, use the model function with the doTraining option set to false.

doTraining = false;
[dlYPred,anglesPred] = model(dlXTest, parameters,doTraining,state);

Evaluate the classification accuracy.

[~,idx] = max(extractdata(dlYPred),[],1);
labelsPred = classNames(idx);
accuracy = mean(labelsPred==YTest)
accuracy = 0.9852

Evaluate the regression accuracy.

angleRMSE = sqrt(mean((extractdata(anglesPred) - anglesTest').^2))
angleRMSE =

  gpuArray single

   10.4900

View some of the images with their predictions. Display the predicted angles in red and the correct labels in green.

idx = randperm(size(XTest,4),9);
figure
for i = 1:9
    subplot(3,3,i)
    I = XTest(:,:,:,idx(i));
    imshow(I)
    hold on
    
    sz = size(I,1);
    offset = sz/2;
    
    thetaPred = extractdata(anglesPred(idx(i)));
    plot(offset*[1-tand(thetaPred) 1+tand(thetaPred)],[sz 0],'r--')
    
    thetaValidation = anglesTest(idx(i));
    plot(offset*[1-tand(thetaValidation) 1+tand(thetaValidation)],[sz 0],'g--')
    
    hold off
    label = string(labelsPred(idx(i)));
    title("Label: " + label)
end

Model Function

The function model takes the input data dlX, the model parameters parameters, the flag doTraining which specifies whether to model should return outputs for training or prediction, and the network state state. The network outputs the predictions for the labels, the predictions for the angles, and the updated network state.

function [dlY1,dlY2,state] = model(dlX,parameters,doTraining,state)

% Convolution
weights = parameters.conv1.Weights;
bias = parameters.conv1.Bias;
dlY = dlconv(dlX,weights,bias,'Padding',2);

% Batch normalization, ReLU
offset = parameters.batchnorm1.Offset;
scale = parameters.batchnorm1.Scale;
trainedMean = state.batchnorm1.TrainedMean;
trainedVariance = state.batchnorm1.TrainedVariance;

if doTraining
    [dlY,trainedMean,trainedVariance] = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
    
    % Update state
    state.batchnorm1.TrainedMean = trainedMean;
    state.batchnorm1.TrainedVariance = trainedVariance;
else
    dlY = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
end

dlY = relu(dlY);

% Convolution, batch normalization (Skip connection)
weights = parameters.convSkip.Weights;
bias = parameters.convSkip.Bias;
dlYSkip = dlconv(dlY,weights,bias,'Stride',2);

offset = parameters.batchnormSkip.Offset;
scale = parameters.batchnormSkip.Scale;
trainedMean = state.batchnormSkip.TrainedMean;
trainedVariance = state.batchnormSkip.TrainedVariance;

if doTraining
    [dlYSkip,trainedMean,trainedVariance] = batchnorm(dlYSkip,offset,scale,trainedMean,trainedVariance);
    
    % Update state
    state.batchnormSkip.TrainedMean = trainedMean;
    state.batchnormSkip.TrainedVariance = trainedVariance;
else
    dlYSkip = batchnorm(dlYSkip,offset,scale,trainedMean,trainedVariance);
end

% Convolution
weights = parameters.conv2.Weights;
bias = parameters.conv2.Bias;
dlY = dlconv(dlY,weights,bias,'Padding',1,'Stride',2);

% Batch normalization, ReLU
offset = parameters.batchnorm2.Offset;
scale = parameters.batchnorm2.Scale;
trainedMean = state.batchnorm2.TrainedMean;
trainedVariance = state.batchnorm2.TrainedVariance;

if doTraining
    [dlY,trainedMean,trainedVariance] = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
    
    % Update state
    state.batchnorm2.TrainedMean = trainedMean;
    state.batchnorm2.TrainedVariance = trainedVariance;
else
    dlY = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
end

dlY = relu(dlY);

% Convolution
weights = parameters.conv3.Weights;
bias = parameters.conv3.Bias;
dlY = dlconv(dlY,weights,bias,'Padding',1);

% Batch normalization
offset = parameters.batchnorm3.Offset;
scale = parameters.batchnorm3.Scale;
trainedMean = state.batchnorm3.TrainedMean;
trainedVariance = state.batchnorm3.TrainedVariance;

if doTraining
    [dlY,trainedMean,trainedVariance] = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
    
    % Update state
    state.batchnorm3.TrainedMean = trainedMean;
    state.batchnorm3.TrainedVariance = trainedVariance;
else
    dlY = batchnorm(dlY,offset,scale,trainedMean,trainedVariance);
end

% Addition, ReLU
dlY = dlYSkip + dlY;
dlY = relu(dlY);

% Fully connect (angles)
weights = parameters.fc1.Weights;
bias = parameters.fc1.Bias;
dlY2 = fullyconnect(dlY,weights,bias);

% Fully connect, softmax (labels)
weights = parameters.fc2.Weights;
bias = parameters.fc2.Bias;
dlY1 = fullyconnect(dlY,weights,bias);
dlY1 = softmax(dlY1);

end

Model Gradients Function

The modelGradients function, takes a mini-batch of input data dlX with corresponding targets T1 and T2 containing the labels and angles, respectively, and returns the gradients of the loss with respect to the learnable parameters, the updated network state, and the corresponding loss.

function [gradients,state,loss] = modelGradients(dlX,T1,T2,parameters,state)

doTraining = true;
[dlY1,dlY2,state] = model(dlX,parameters,doTraining,state);

lossLabels = crossentropy(dlY1,T1);
lossAngles = mse(dlY2,T2);

loss = lossLabels + 0.1*lossAngles;
gradients = dlgradient(loss,parameters);

end

Weights Initialization Function

The initializeGaussian function samples weights from a Gaussian distribution with mean 0 and standard deviation 0.01.

function parameter = initializeGaussian(sz)
parameter = randn(sz,'single').*0.01;
end

See Also

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