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An autoencoder (AE) and a generative adversarial networks (GANs) are trainedonly once on a one-dimensional (1D) lattice of 200 sites. Moreover, the AEcontains only one hidden layer consisting of two neurons and both the generatorand the discriminator of the GANs are made up of two neurons as well. Thetraining set employed to train both the considered unsupervised neural networks(NN) is composed of two artificial configurations. Remarkably, despite theirsimple architectures, both the built AE and GANs have precisely determined thecritical points of several models, including the three-dimensional (3D)classical $O(3)$ model, the two-dimensional (2D) generalized classical XYmodel, the 2D two-state Potts model, and the 1D Bose-Hubbard model. The resultspresented here as well as that shown in {\it Eur. Phys. J. Plus {\bf 136}, 1116(2021)} suggest that when phase transitions are considered, an elegantuniversal neural network that is extremely efficient and is applicable to broadphysical systems can be constructed with ease. In particular, since a NNtrained with two configurations can be applied to many models, it is likelythat when machine learning is concerned, the majority of phase transitionsbelong to a class having two elements, i.e. the Ising class.

A universal training scheme and the resulting universality for machine learning phases