Make Predictions Using dlnetwork Object

Load dlnetwork Object

Load a trained dlnetwork object and the corresponding class names into the workspace. The neural network has one input and two outputs. It takes images of handwritten digits as input, and predicts the digit label and angle of rotation.

load dlnetDigits

Load Data for Prediction

Load the digits test data for prediction.

load DigitsDataTest

View the class names.

classNames

classNames = 10×1 cell
    {'0'}
    {'1'}
    {'2'}
    {'3'}
    {'4'}
    {'5'}
    {'6'}
    {'7'}
    {'8'}
    {'9'}

View some of the images and the corresponding labels and angles of rotation.

numObservations = size(XTest,4);
numPlots = 9;
idx = randperm(numObservations,numPlots);

figure
for i = 1:numPlots
    nexttile(i)
    I = XTest(:,:,:,idx(i));
    label = labelsTest(idx(i));
    imshow(I)
    title("Label: " + string(label) + newline + "Angle: " + anglesTest(idx(i)))
end

Make Predictions

Make predictions using the minibatchpredict function, and convert the classification scores to labels using the scores2label function. By default, the minibatchpredict function uses a GPU if one is available. Using a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). Otherwise, the function uses the CPU. To select the execution environment manually, use the ExecutionEnvironment argument of the minibatchpredict function.

[scoresTest,Y2Test] = minibatchpredict(net,XTest);
Y1Test = scores2label(scoresTest,classNames);

Visualize some of the predictions.

idx = randperm(numObservations,numPlots);

figure
for i = 1:numPlots
    nexttile(i)
    I = XTest(:,:,:,idx(i));
    label = Y1Test(idx(i));
    imshow(I)
    title("Label: " + string(label) + newline + "Angle: " + Y2Test(idx(i)))
end