jstoolbox

 Given an Untrained Neural Network, it will have its own tendency of scoring. If we use multiples Untrained Neural Networks to give us scores to train a master Neural Net then it seems too be too random. As features high scoring in one system could produce low scoring in another System. So the idea I have here is that I would feed the Untrained Neural Networks my set of data that doesn't change. But for the first 2 items: the first item has a low rating based on my taste, and 2nd item has a high rating based on my taste. So with these two items we have a direction from bad to good. Then we feed these 2 items along with other random data into the untrained networks. Then we check the untrained networks prediction. if it's in the same direction mean prediction for 2nd item is higher than 1st item then we leave it. If not it means that the untrained network has kind'a like opposite taste from my ratings.  So we flip it's scoring and use that data. This is an idea to have t

Ok, so I got this some what dumb brain that has preferences. And training a neutral net to shape it to closely to my preferences will only give have the tendency to show me what I like. And I think my general liking is preventing the NN to give surprising results. What if? you set up an NN, not train it at all, leave it as is. like an untrained mind or a random configuration. It still outputs whatever heck it does who knows but then you look at the lowest scores and highest scores and see what it produces. My hope is that in its own configuration, the top and bottom scores have to point to something some sort of direction (maybe garbage, maybe strange, but top and bottom must be either super garbage or super strange). I just thought of this because I was thinking back to Alpha Go how it beat human with a move that people didn't see before. And how when they trained it without human data it performed better on its own. It's not like that at all but that Alpha Go gave me ideas.

 I chose these ones after looking at the top 100 from millions of random ones.

 These were picked by me from the top 50s or so (from the best scoring out of a pool of 10,000,000 random variations). This was trained using 1354 of my own votes on random ones (scores were mostly between 0 and 3 some are 4,5 and 6, 7, 8 but very few like maybe 20 of them out of the whole 1354). There's just so many random ones that I don't like. Selected Piece #1 Selected Piece #2 Selected Piece #3 Selected Piece #4 Selected Piece #5 I still think I need to give it more training data. as I see improvement in taste compared to this post on reddit

After posting...9 different pieces on /r/generative  and asking for feed back. I got some. A redditor mentioned distorting the texture so it doesn't look too much like the source photo, I thought I'll just scanline distorted (since I am not sure what distortion means in this case I am guessing mess it up a little). So here it is using scanline distortion, just swap randomly  lines within a specified range. So from this I made a scanline distortion tool for myself or whoever wants to use. With distortion and Missing tiles  Along, with this change I made my image tile shuffler to also allow for missing tiles generation. (I personally prefer to see some missing tiles, not sure why, it just looks more interesting). Thanks to the redditor who suggested that. I like this one way more than the original texture. And below I am trying to message with breaks in the shape and slighting different sizes and doodled outlines. And no feedback on this one but I like the greens so I played wi

Scanlines Random Swap Distortion You can specify the maximum range for randomize swapping Y-scanlines Just load an image by choosing a file from your computer, then click Distort. You can change the Max Range and click Distored again and again. Max Range for Distortion (scanlines range to randomize with)