1-d input nmr signal use resnet18 to denoise in matlab in matlab

To denoise 1-D input NMR signal using ResNet18 in MATLAB, follow the steps below:

1. Load the dataset of NMR signals.
2. Preprocess the dataset by normalizing and splitting it into training and test sets.
3. Define the ResNet18 architecture using the "resnet18" function in MATLAB.
4. Modify the architecture to accept 1-D input signals by replacing the first layer with a "Fully Connected" layer with the appropriate input size.
5. Train the ResNet18 model on the training set using the "trainNetwork" function in MATLAB.
6. Use the trained ResNet18 model to denoise a test NMR signal by passing it through the model using the "predict" function in MATLAB.

Here is an example code snippet to get started:

main.m
% Load dataset
load nmr_dataset.mat % replace with your own dataset

% Preprocess data
x_train = normalize(train_data);
x_test = normalize(test_data);

% Define ResNet18 architecture
net = resnet18;

% Modify architecture for 1-D input signals
input_sz = size(x_train,2); % assuming each signal has the same length
layers = net.Layers;
layers(1) = fullyConnectedLayer(input_sz, 'Name', 'fc'); % replace first layer
net = assembleNetwork(layers);

% Train ResNet18 model
options = trainingOptions('adam', 'MaxEpochs', 10, 'MiniBatchSize', 32);
trained_net = trainNetwork(x_train, x_train, net, options);

% Denoise test signal
denoised_signal = predict(trained_net, x_test(1,:)); % assuming first test signal

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Note: This is just an example code snippet and you will need to modify it to fit your specific use case.