Spoiler: Examples of "bad"

Spoiler: Examples of "good"

Spoiler: Maps examples





















Hello, in the last year I decided to make several galaxies for Stellaris.In the Stellaris API you need to set the position of each star.Doing this manually is too boring, so I thought that I needed some kind of rule that could generate something that was not boring enough.Someone at this forum advised me to use fractals I opened the page on the wiki, read what it was and created my own program I decided to use a Julia Set , it depends mainly on the complex constant C, which I first took from the wiki examples.So I made the first my maps/galaxies. They didn't look bad, although they had more stars than they should have.Then I thought: select number C manually too long, I'll try to generate it randomly.And as a result, I got many ellipses.Then I decided to go "deeper" into the set and take only the site.And I got a lot of "pieces" of some figures instead of something interesting, about 80% were rubbish. But there were 20% of images that did not look bad as galaxies.At that moment, I did not think of anything better than sorting manually, but it's pretty tedious.Then I heard about neural networks; If this dark magic can find and recognize people, then it would cope well with my task.I used the TFLearn library, which provides a simpler work with the Google Neural library TensorFlow And so I sent my program to friends and collected tribute of pictures from them.It turned out 1200 images, of which 200-400 were good.So I trained the first convolutional neural network It gave about 80% of correct answers.Because it always answered 0 lol.I thought and decided to take for training the same number of "bad" and "good" images. That helped.With the Genetic Algorithm , I was able to select best parameters in which the network gave 90% of correct answers, but this was not very satisfying.I then created another 5,000 images, sorted them by the network and went by hand, correcting 10% of the incorrect classifications.Neural networks work in such a way that the more data you have, the better the result.So, after having generated 5,000 images twice and sorted them out, I received about 13,000 good and bad preview images for galaxies.That gave for the new network the accuracy of classification about 99.74% on the test set.And now in my mod 6059 galaxies.Thanks for your attention to my little research.Thanks Ninja Ferret for grammar check.