  We design a Convolutional Neural Network (CNN) which studies correlation
between discretized inverse temperature and spin configuration of 2D Ising
model and show that it can find a feature of the phase transition without
teaching any a priori information for it. We also define a new order parameter
via the CNN and show that it provides well approximated critical inverse
temperature. In addition, we compare the activation functions for convolution
layer and find that the Rectified Linear Unit (ReLU) is important to detect the
phase transition of 2D Ising model.
