This repository contains the code and data used for training CNN-based encoder-decoder model, described in the paper: "Noise reduction in X‑ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models" by T.Konstantinova, L.Wiegart, M.Rakitin, A.M.DeGennaro and A.M.Barbour. https://doi.org/10.1038/s41598-021-93747-y
Files descriptions:
run_training.py
-- the main script to assign the parameters of the model and to run the model. This script calls the initiaion of the data sets and the model (from the fileutils.py
) and training of the moodel (from the filenets.py
);nets.py
-- the class for the autoencoder model;train_and_test.py
-- functions for model training, validation and testing;utils.py
-- auxiliary functions for model assembly, fixing the random seed, data loader, etc.requirements.txt
files with required libraries for the scripts to run.
To run the script, type in the terminal:
>> conda create --name cnn-training
>> conda activate cnn-training
>> conda install pip
>> pip install -r requirements.txt
>> python run_training.py
Link for the data:
The folder data/ contains the data for training and testing the model.