Make Predictions Using Model Function

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This example shows how to make predictions using a model function by splitting data into mini-batches.

For large data sets, or when predicting on hardware with limited memory, make predictions by splitting the data into mini-batches. When making predictions with SeriesNetwork or DAGNetwork objects, the predict function automatically splits the input data into mini-batches. For model functions, you must split the data into mini-batches manually.

Create Model Function and Load Parameters

Load the model parameters from the MAT file digitsMIMO.mat. The MAT file contains the model parameters in a struct named parameters, the model state in a struct named state, and the class names in classNames.

s = load("digitsMIMO.mat");
parameters = s.parameters;
state = s.state;
classNames = s.classNames;

The model function model, listed at the end of the example, defines the model given the model parameters and state.

Load Data for Prediction

Load the digits data for prediction.

digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
    'nndatasets','DigitDataset');
imds = imageDatastore(digitDatasetPath, ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');

Make Predictions

Loop over the mini-batches of the test data and make predictions using a custom prediction loop.

For each mini-batch:

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

  • Make predictions 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.

  • Make predictions by calling the model function with the doTraining option set to false.

  • Determine the class labels by finding the maximum scores.

miniBatchSize = 128;
executionEnvironment = "auto";
doTraining = false;

imds.ReadSize = miniBatchSize;
numObservations = numel(imds.Files);
Y1Pred = strings(1,numObservations);
Y2Pred = zeros(1,numObservations);
i = 1;

% Loop over mini-batches.
while hasdata(imds)
    
    % Read mini-batch of data.
    data = read(imds);
    X = cat(4,data{:});
    
    % Normalize the images.
    X = single(X)/255;
    
    % Convert mini-batch of data to dlarray.
    dlX = dlarray(X,'SSCB');
    
    % If making predictions on a GPU, then convert data to gpuArray.
    if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
        dlX = gpuArray(dlX);
    end
    
    % Make predictions using the predict function.
    [dlY1Pred,dlY2Pred] = model(dlX,parameters,doTraining,state);
    
    % Determine corresponding classes.
    [~,idxTop] = max(extractdata(dlY1Pred),[],1);
    idxMiniBatch = i:min((i+miniBatchSize-1),numObservations);
    Y1Pred(idxMiniBatch) = classNames(idxTop);
    
    Y2Pred(idxMiniBatch) = gather(extractdata(dlY2Pred));
    
    i = i + miniBatchSize;
end

View some of the images with their predictions.

idx = randperm(numel(imds.Files),9);
figure
for i = 1:9
    subplot(3,3,i)
    I = imread(imds.Files{idx(i)});
    imshow(I)
    hold on
    
    sz = size(I,1);
    offset = sz/2;
    
    thetaPred = Y2Pred(idx(i));
    plot(offset*[1-tand(thetaPred) 1+tand(thetaPred)],[sz 0],'r--')
        
    hold off
    label = string(Y1Pred(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
W = parameters.conv1.Weights;
B = parameters.conv1.Bias;
dlY = dlconv(dlX,W,B,'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)
W = parameters.convSkip.Weights;
B = parameters.convSkip.Bias;
dlYSkip = dlconv(dlY,W,B,'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
W = parameters.conv2.Weights;
B = parameters.conv2.Bias;
dlY = dlconv(dlY,W,B,'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
W = parameters.conv3.Weights;
B = parameters.conv3.Bias;
dlY = dlconv(dlY,W,B,'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)
W = parameters.fc1.Weights;
B = parameters.fc1.Bias;
dlY2 = fullyconnect(dlY,W,B);

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

end

See Also

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