“Auto clustering of galaxies after dimensionality reduction”的版本间差异

来自cluster
跳到导航 跳到搜索
无编辑摘要
无编辑摘要
第1行： 第1行：
This work is divided into two parts.
This work is divided into two parts.
The first part is to reduce the dimension of Galaxy data to low dimensional space with VAE.
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. As shown in the example of text in figure 1 below:
(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. As shown in the example of text in the figure below:
⚫
From more than 300，000 data, 290613 galaxies data matching the shape conditions were selected.
[[File:微信图片_20220304222539.png|500px|center]]
[[File:微信图片_20220304222539.png|500px|center]]
⚫
From more than 300，000 data, 290613 galaxies data matching the shape conditions were selected.
(2)

2022年3月4日 (五) 14:18的版本

 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. As shown in the example of text in the figure below:
微信图片 20220304222539.png
 From more than 300，000 data, 290613 galaxies data matching the shape conditions were selected.
 (2)
取自“http://cluster.shao.ac.cn/wiki/index.php?title=Auto_clustering_of_galaxies_after_dimensionality_reduction&oldid=4768”