how to make cnn for mnist in matlab

Here is a sample code in MATLAB for creating a CNN for image classification on the MNIST dataset:

main.m
% Load the MNIST data
[XTrain, YTrain, XTest, YTest] = helperMNISTData.load;

% Define the layers of the CNN
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(5, 20)
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(5, 50)
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(500)
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

% Train the CNN
options = trainingOptions('sgdm', ...
    'InitialLearnRate', 0.01, ...
    'MaxEpochs', 4, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {XTest, YTest}, ...
    'ValidationFrequency', 30, ...
    'Verbose', false, ...
    'Plots', 'training-progress');
trainedNet = trainNetwork(XTrain, YTrain, layers, options);

% Test the CNN
YPred = classify(trainedNet, XTest);
accuracy = sum(YPred == YTest)/numel(YTest);
disp("Accuracy = " + accuracy)

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In this code, the helperMNISTData.load function is used to load the MNIST dataset. The layers of the CNN are defined using imageInputLayer, convolution2dLayer, reluLayer, maxPooling2dLayer, fullyConnectedLayer, softmaxLayer, and classificationLayer. The training options are specified using trainingOptions, and the CNN is trained using trainNetwork. Finally, the accuracy of the CNN is computed using the classify function and compared to the true labels YTest.

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related categories

matlab
convolutional-neural-network
mnist
deep-learning
image-classification

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