clean nmr datas as a cnn' output and the datas contains noise as its input to train the cnn in matlab

To clean NMR (nuclear magnetic resonance) data as a CNN (convolutional neural network) output and train the network using noisy input data, the following steps can be taken in MATLAB:

First, import the NMR dataset into MATLAB.
Preprocess the data by removing any unwanted noise using standard signal processing techniques such as filtering or denoising algorithms.
Split the dataset into training and testing sets.
Define the CNN architecture using MATLAB's Deep Learning Toolbox.
Train the CNN using the training set.
Evaluate the performance of the CNN on the testing set.
Fine-tune the CNN if necessary to improve its performance.
Use the trained CNN to predict and clean new NMR data.

The following code provides an example of how to define a simple CNN architecture and train it on noisy NMR data:


main.m
% Load and preprocess the NMR dataset
data = load('nmr_data.mat');
noisy_input = data.noisy_data;
clean_output = data.clean_data;

% Define the CNN architecture
layers = [
    imageInputLayer([size(noisy_input,1) size(noisy_input,2) 1])
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];

% Define the training options
options = trainingOptions('adam', ...
    'InitialLearnRate',0.001, ...
    'MaxEpochs',30, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{noisy_input_test,clean_output_test}, ...
    'Plots','training-progress');

% Train the CNN on the noisy NMR data
net = trainNetwork(noisy_input_train,clean_output_train,layers,options);

% Evaluate the performance of the CNN on the testing set
predicted_output = predict(net,noisy_input_test);
accuracy = mean(abs(predicted_output - clean_output_test));
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This code defines a simple CNN architecture consisting of two convolutional layers, two pooling layers, and a fully connected layer. The network is trained using the noisy NMR data and then used to predict and clean new input data.

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