using noise nmr sequences as a cnn-resnet input wish the corresponding nmr sequences as the cnn-resnet output in matlab

There are multiple ways you can accomplish this task depending on your specific needs. However, here is a general approach you can follow:

  1. Import your NMR spectra data from files or generate synthetic spectra depending on your application.
  2. Preprocess your data such as normalize and remove the baseline.
  3. Reshape your input NMR data to have three dimensions: [Number of spectra, Number of data points, Number of channels]
  4. You can consider using the pre-trained 1D CNN models on a large dataset or you can train your own CNN model.
  5. Train your CNN model, and use the corresponding NMR spectra data as target outputs.
  6. Once you have a trained model, you can use it to predict the output of new unseen input NMR data.

Here is an example code to define a 1D CNN ResNet model with several layers:

main.m
function layers = resNet1D(num_classes)

    % Input layer with data dimensions [1, 2000, 1]
    inputLayer = imageInputLayer([1 2000 1]);

    % First identity residual block with 16 filters
    residualBlock1 = [
        convolutionalLayer(7, 16, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(5, 16, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(3, 16, 'Padding', 'same')
        batchNormalizationLayer];

    % Second identity residual block with 32 filters
    residualBlock2 = [        
        convolutionalLayer(7, 32, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(5, 32, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(3, 32, 'Padding', 'same')
        batchNormalizationLayer];

    % Third identity residual block with 64 filters
    residualBlock3 = [        
        convolutionalLayer(7, 64, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(5, 64, 'Padding', 'same')
        batchNormalizationLayer
        reluLayer  
        convolutionalLayer(3, 64, 'Padding', 'same')
        batchNormalizationLayer];

    % Global max pooling and fully connected layer with 64 channels
    finalLayers = [
        globalMaxPoolingLayer
        fullyConnectedLayer(64)
        reluLayer
        fullyConnectedLayer(num_classes)
        softmaxLayer
        classificationLayer];

    % Connect all layers to form the CNN model
    layers = [
        inputLayer
        residualBlock1
        additionLayer(2,'Name','res1')
        reluLayer
        residualBlock2
        additionLayer(2,'Name','res2')
        reluLayer 
        residualBlock3
        additionLayer(2,'Name','res3')
        reluLayer
        finalLayers];

end

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Please note that this example model is just to give you an idea of the structure of the model, you may need to add additional layers, change the filter sizes, or change the number of filters in each layer based on your specific requirements.

Finally, note that this approach requires a lot of computing resources and may take a long time to converge, especially if you have a large dataset. You may need to optimize the model structures or use pre-trained models to save training time.

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