Fig. 1: Regression CNN architecture for Ni phantom experimental system featuring a single phase.

CONV represents 1-D convolutional layers, Pool represents max-pooling layers, FC represents fully connected layers, and Dropout represents dropout layers with 10% dropout rate. The network consists of eight convolutional layers, six max-pooling layers, one flatten layer, nine fully connected layers and six dropout layers in total. There are three routes connected to the flatten layer which give predictions for scale factor, crystallite size and lattice parameter a, respectively. Each route has three fully connected layers whose scales are shown in the figure. The number of filters, kernel sizes and the stride of convolutional layers are also given in the figure. All max-pooling layers have stride equal to 2.