train a convolutional neural network in matlab

To train a convolutional neural network (CNN) in MATLAB, you can follow the steps given below:

1. Load and prepare the dataset: The first step is to load and prepare the dataset. You can use the imageDatastore class in MATLAB to load the images and label them accordingly.

main.m
imds = imageDatastore('path\to\dataset', 'IncludeSubfolders',true, 'LabelSource','foldernames');
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2. Define the CNN architecture: Once the dataset is loaded and prepared, you can define the CNN architecture. You can use the CNN architecture app in MATLAB to create the architecture or define it programmatically using the layers API.

main.m
layers = [
    imageInputLayer([32 32 3])
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];
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3. Specify the training options: Next, you need to specify the training options such as the optimization algorithm, the learning rate, the mini-batch size, and the number of epochs.

main.m
opts = trainingOptions('adam', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',40, ...
    'MiniBatchSize',128, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
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4. Train the CNN: Finally, you can train the CNN using the trainNetwork function in MATLAB.

main.m
net = trainNetwork(imds,layers,opts);
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This will start the training process and display the progress in the command window. Once the training is complete, you can use the trained CNN to classify new images.

main.m
YPred = classify(net,imdsTest);
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This is a basic example of how to train a CNN in MATLAB. Depending on your specific use case, you may need to modify the CNN architecture, the training options, or the data preparation steps to achieve better performance.