the nmr sequence wavelet coefficients are input as cnn-resnet, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet output for denoising in matlab

To use wavelet coefficients as input to a CNN-ResNet in Matlab, you can follow the following steps:

1. Load and Normalize Data: Load the wavelet coefficients of the NMR sequence and normalize the data. You can use z-score normalization which calculates mean and standard deviation of data and scales the data to have zero mean and unit standard deviation.

   main.m
    load("nmr_wavelet_coeffs.mat")
    % normalizing the data 
    coeffs = zscore(nmr_wavelet_coeffs);
   95 chars
   4 lines

2. Prepare Data for Training: Divide the data set into training, validation and test sets.

   main.m
   %Number of samples
   N = size(coeffs,1);   
   %Time step
   L = size(coeffs,2);  
   % Dividing the data as training, validation, and testing sets. 
   numTrain = round(N*0.60);
   numVal = round(N*0.20);
   numTest = N-numTrain-numVal;
   
   XTrain = coeffs(1:numTrain,1:L-1);
   YTrain = coeffs(1:numTrain,L);
   XValidation = coeffs(numTrain+1:numTrain+numVal,1:L-1);
   YValidation = coeffs(numTrain+1:numTrain+numVal,L);
   XTest = coeffs(numTrain+numVal+1:end,1:L-1);
   YTest = coeffs(numTrain+numVal+1:end,L);
   479 chars
   16 lines

3. Define Layers: Define the layers of the CNN-ResNet. Add Convolution 1D layers, Relu Activation layers, Batch Normalization layers, Residual Connection blocks and Dense layers.

   main.m
    % define layers
    layers = [
        sequenceInputLayer(L-1,'Name','input')   
        convolution1dLayer(32, 4, 'Padding', 'same', 'Name', 'conv1') 
        batchNormalizationLayer('Name','BN1')
        reluLayer('Name','relu1')
        convolution1dLayer(64, 4, 'Padding', 'same', 'Name', 'conv2') 
        batchNormalizationLayer('Name','BN2')
        reluLayer('Name','relu2')
        convolution1dLayer(128, 4, 'Padding', 'same', 'Name', 'conv3') 
        batchNormalizationLayer('Name','BN3')
        reluLayer('Name','relu3')
        convolution1dLayer(256, 4, 'Padding', 'same', 'Name', 'conv4') 
        batchNormalizationLayer('Name','BN4')
        reluLayer('Name','relu4')
        sequenceFoldingLayer('Name','fold')
        ];
   
    b = [
        batchNormalizationLayer('Name','BN5')
        reluLayer('Name','relu5')
        convolution1dLayer(1, 4, 'Padding', 'same', 'Name', 'conv5') 
        regressionLayer('Name','output')
        ];
   
    layers = [layers b];
   913 chars
   27 lines

4. Train the model: Train the CNN-ResNet model using training and validation data.

   main.m
   options = trainingOptions("adam", ...
    "ExecutionEnvironment",'auto', ...
    "MaxEpochs",30, ...
    "MiniBatchSize",256, ...     
    "SequenceLength",'longest', ...
    "Shuffle","every-epoch", ...
    "ValidationData",{XValidation,YValidation}, ...
    "ValidationFrequency",10, ...
    "ValidationPatience",Inf, ...
    "Verbose",false, ...
    "Plots","training-progress");
        
   
    net = trainNetwork(XTrain',YTrain',layers,options);
   413 chars
   15 lines

5. Test the model: Test the model using the test data.

   main.m
   YPred = classify(net,XTest');
   YTest = categorical(YTest);
   
   accuracy = sum(YPred==YTest)/numel(YTest);
   102 chars
   5 lines

6. Evaluate the model: Evaluate the model using different metrics like Mean Squared Error (MSE).

   main.m
    YPred = predict(net,XTest');
    MSE = mean((YPred-YTest').^2);
   62 chars
   3 lines

This code will create a CNN-ResNet model for denoising the NMR sequence wavelet coefficients in Matlab.