neuralnet for calculating the percentage of input image and the bluered image in matlab

To calculate the percentage of input image and the bluered image using a neural network model in Matlab, you can follow these steps:

1. Prepare your dataset: Prepare your dataset by collecting and labeling your input images and bluered images. Each image should be resized to the same size, and its pixel values should be normalized to the range [0, 1]. Split your dataset into training, validation, and testing sets.

2. Define and design the neural network model: Define the architecture of your neural network. You can use a convolutional neural network (CNN) to extract features from the input images, followed by fully connected (FC) layers to predict the percentage of each image. You can design your own model using the neuralNetworkDesigner app in Matlab.

3. Train the neural network model: Use the training set to train your neural network model. You can use the trainNetwork function to train your model. Specify the network architecture, the training set, the validation set, the learning rate, the plotters, and the training options.

main.m
% Define the network architecture
layers = [...];

% Specify the training options
options = trainingOptions('sgdm', 'MaxEpochs', ..., 'MiniBatchSize', ..., 'ValidationData', ..., 'Plots', 'training-progress');

% Train the network
net = trainNetwork(trainingData, layers, options);
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4. Test and evaluate the neural network model: Use the testing set to test your neural network model. You can use the classify function to predict the percentage of each image. Evaluate the performance of your model by computing the accuracy, the confusion matrix, and the ROC curve.

main.m
% Test the network
YPred = classify(testData, net);

% Compute the accuracy
accuracy = sum(YPred == testLabels)/numel(testLabels);

% Compute the confusion matrix
confMat = confusionmat(testLabels, YPred);

% Compute the ROC curve
[X,Y] = perfcurve(testLabels, YPred, ...);
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5. Apply the neural network model: Apply your trained neural network model to new input images to predict their percentage of bluered. You can use the classify or predict function to make predictions.

main.m
% Apply the network to new images
newImg1 = imread('newImg1.png');
newImg2 = imread('newImg2.png');

newImg1 = imresize(newImg1, [224 224]);
newImg2 = imresize(newImg2, [224 224]);

newImg1 = im2double(newImg1);
newImg2 = im2double(newImg2);

percentage1 = classify(net, newImg1);
percentage2 = predict(net, newImg2);
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