Training and testing XRD dataset for crystallite size and microstrain determination using deep neural networks

Numpy tensors to train and test a convolutional neural network dedicated to determine crystallite size and/or microstrain from X-ray diffraction data (XRD): train_size.npz: training dataset with only crystallite size test_size.npz: testing dataset with only crystallite size train_size_strain.npz: training dataset with crystallite size and microstrain test_size_strain.npz: testing dataset with crystallite size and microstrain Each dataset contains the XRD data and the labels ("ground truth") in the form of 2D tensors with 10501 data points (columns) for the XRD data, and 24 labels (columns) for the labels. Training data contain 71971 rows ; testing data contain 7997 rows. Example python script to read the data: import numpy as np train = np.load("train_size.npz") train_data, train_label = train["train_data"], train["train_label"] print(f"Train data shape: {train_data.shape}, Train labels shape: {train_label.shape}") Jupyter notebooks to train and test a neural network can be found here: https://github.com/aboulle/LPA-NN