This is sample code.. Try it.
% Load your dataset and ground truth data here
% Replace 'input_images' and 'ground_truth' with your actual data
% 'input_images' should be a 4D array with size [height x width x channels x numPairs]
% 'ground_truth' should be a 2D array with size [numPairs x 6] representing the 6x1 array of translations and rotations
% Split the data into training, validation, and testing sets
numPairs = size(input_images, 4);
numTraining = round(0.7 * numPairs);
numValidation = round(0.2 * numPairs);
numTesting = numPairs - numTraining - numValidation;
trainingData = input_images(:, :, :, 1:numTraining);
trainingLabels = ground_truth(1:numTraining, :)';
validationData = input_images(:, :, :, numTraining+1:numTraining+numValidation);
validationLabels = ground_truth(numTraining+1:numTraining+numValidation, :)';
testingData = input_images(:, :, :, numTraining+numValidation+1:end);
testingLabels = ground_truth(numTraining+numValidation+1:end, :)';
% Create the CNN architecture
layers = [
imageInputLayer([size(input_images, 1) size(input_images, 2) size(input_images, 3) 2]) % Input layer with two images
convolution2dLayer(3, 16, 'Padding', 'same', 'Name', 'conv1') % Convolutional layer with 16 filters of size 3x3
batchNormalizationLayer('Name', 'bn1') % Batch normalization
reluLayer('Name', 'relu1') % ReLU activation
maxPooling2dLayer(2, 'Stride', 2, 'Name', 'maxpool1') % Max pooling
convolution2dLayer(3, 32, 'Padding', 'same', 'Name', 'conv2') % Convolutional layer with 32 filters of size 3x3
batchNormalizationLayer('Name', 'bn2') % Batch normalization
reluLayer('Name', 'relu2') % ReLU activation
maxPooling2dLayer(2, 'Stride', 2, 'Name', 'maxpool2') % Max pooling
fullyConnectedLayer(128, 'Name', 'fc1') % Fully connected layer with 128 neurons
reluLayer('Name', 'relu3') % ReLU activation
fullyConnectedLayer(6, 'Name', 'fc2') % Output layer with 6 neurons (translations and rotations)
regressionLayer('Name', 'output') % Regression layer for continuous output
];
% Set the training options
options = trainingOptions('adam', ...
'MaxEpochs', 20, ...
'MiniBatchSize', 32, ...
'ValidationData', {validationData, validationLabels}, ...
'Plots', 'training-progress');
% Create and train the CNN
net = trainNetwork(trainingData, trainingLabels, layers, options);
% Test the CNN on the testing dataset
predictedLabels = predict(net, testingData);
% Calculate the Mean Squared Error (MSE) between predicted and ground truth labels
mse = mean((predictedLabels - testingLabels).^2);
disp(['Mean Squared Error: ' num2str(mse)]);
