Auto clustering of galaxies after dimensionality reduction

 This work is divided into two parts.
 The first part is to reduce the dimension of Galaxy data to low dimensional space with VAE.
 (1) In the first step, we first filter out the galaxy data with data shape [3*256*256], and save the galaxy data paths that match this shape into a text file, which constitutes our training set.
 From more than 300，000 data, 290613 galaxies data matching the shape conditions were selected.