train a dlnetwork in matlab

To train a DL network in MATLAB, the most basic steps are:

1. Load and preprocess the data
2. Create a deep learning neural network using the deepNetworkDesigner app or by coding it in MATLAB
3. Train the neural network using the trainNetwork function
4. Evaluate the performance of the trained network using the classify or predict functions.

Here's a sample code on how to train a convolutional neural network (CNN) using the trainNetwork function in MATLAB:

main.m
% Load training and testing data
imdsTrain = imageDatastore('path/to/training/images', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
imdsTest = imageDatastore('path/to/testing/images', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

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

% Define training options
options = trainingOptions('sgdm',...
    'MaxEpochs',20,...
    'ValidationData',imdsTest,...
    'ValidationFrequency',30,...
    'Verbose',false,...
    'Plots','training-progress');

% Train the CNN
net = trainNetwork(imdsTrain,layers,options);

% Test the trained CNN on new data
predictedLabels = classify(net,imdsTest);
accuracy = mean(predictedLabels == imdsTest.Labels);
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In this example, we used the Stochastic Gradient Descent with Momentum (SGDM) optimizer, the maximum number of epochs as 20, and specified to validate the network on the testing data every 30 iterations. The training-progress option will show the progress of training in a nice progress bar.

You can modify the architecture, as well as the training options, depending on your problem and data. It's always best to experiment with different architectures and parameters to find the best model for your data.