The impact on the performance of an unsupervised neural network (NN) for learning the phases of two-dimensional ferromagnetic Potts model, namely a deep learning autoencoder (AE), from using various training sets is investigated. We find that data below and in the vicinity of the transition temperature Tc are crucial in training a successful AE. Our results also indicate that the commonly employed training procedures for unsupervised NNs are not efficient, and the obtained outcomes here can be considered as useful guidelines to set up effective trainings for unsupervised NNs.
ASJC Scopus subject areas
- Physics and Astronomy(all)