nmr sequences contain noise as a cnn input and the corresponding sequences ad the output in matlab

To use NMR spectra that contain noise as CNN input, you could use the following approach:

Pre-process the spectra: Convert the raw NMR spectra to a matrix form, where each row represents a spectrum and each column represents a peak/bucket.
Add noise to the spectra: Add random noise to the matrix in order to simulate real-world data.
Split the data: Split the dataset into training, validation, and test sets.
Train the CNN: Build and train a CNN using the training set as inputs and the corresponding noise-free spectra as outputs. The aim is to train the CNN to learn the mappings between the noisy spectra and corresponding noise-free spectra.
Validate the CNN: Tune the hyperparameters of the CNN using the validation set.
Test the CNN: Evaluate the performance of the CNN using the test set.

Here's some sample code to get you started:


main.m
% Load the spectra and corresponding clean spectra
load('spectra.mat');
load('clean_spectra.mat');

% Add noise to the input spectra
noisy_spectra = spectra + 0.1*randn(size(spectra));

% Split the dataset into training, validation, and test sets
train_x = noisy_spectra(1:800,:);
train_y = clean_spectra(1:800,:);
val_x = noisy_spectra(801:900,:);
val_y = clean_spectra(801:900,:);
test_x = noisy_spectra(901:end,:);
test_y = noisy_spectra(901:end,:);

% Define the CNN architecture
num_epochs = 50;
batch_size = 32;
input_size = size(train_x);
layers = [
    imageInputLayer(input_size)
    convolution2dLayer(3,32,'Padding','same')
    reluLayer
    convolution2dLayer(3,32,'Padding','same')
    reluLayer
    convolution2dLayer(3,32,'Padding','same')
    reluLayer
    convolution2dLayer(3,1,'Padding','same')
    regressionLayer
];

% Train the CNN
options = trainingOptions('sgdm', ...
    'MaxEpochs',num_epochs, ...
    'MiniBatchSize',batch_size, ...
    'ValidationData',{val_x, val_y}, ...
    'Plots','training-progress');
net = trainNetwork(train_x,train_y,layers,options);

% Test the CNN
test_y_pred = predict(net, test_x);
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This code assumes that you have the input spectra in a variable called 'spectra', the corresponding clean spectra in a variable called 'clean_spectra', and the CN in the last convolutional layer used as a regression loss function. You will need to tweak this code to suit your specific needs.

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