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Yes, androids do dream of electric sheep


zlemflolia

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anyone get it to work yet? im trying ryan kennedys and its throwing an error.. win machine. others have got the same error and fixed it and i cant. damnn

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nada.. and ive done without the inverted comma at the beginning, thats a copy/paste error.

 

 

beginning to get tiring now. ive been at it all day lol

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getting somewhere, you need to close powershell then start it again and it worked


keep your fingers crossed for me guys i dont know wtf im doing

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My iddems are still in queue. So slow.

 

f85b4cd5-182e-4dba-96c1-9a86c6582120.jpg

 

Sean pls

 

212edc95-7157-493a-9c29-e359fbbd3702.jpg

 

Meh

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im going to stick all my artwork in a net and make an ai learn it if i can, then do some weird art with it. fingers crossed. ive got this bit nailed, had to re-do it 5 times so i know what im doing now :) the reddit thread works


my computer is well fast now doing a 640x480 image. woop

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The highest layers of the DNN will ultimately be the training set images (approx), so you'll end up with a lot of dogs and buildings and eyes. The broader the training set is the more it can recognize. To get really trippy images you either need an abstract training set or use lower layers to detect edges and lines etc rather than complete images. Would be cool to mix in images of different layers in DNN of focus. So like one part uses layer 2 amplification, and another part uses layer 4 or whatever, in this way you get different levels of abstraction in the same image. Creativity in humans uses all layers at the same time, but ALSO takes into account what we know about the real world like gravity, objects function etc, then we could have real generation on par with humans. Right now it's just a passive pattern recognizer, not an active pattern recognizer with precision output (like our hands for drawing etc) with knowledge like humans have.

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yeah im speaking with someone whos trying to install the better model set, with less dogs.

 

im going for quality. currently on inception 2 part 6, 22gigs full and no crash yet, 3840x2560. fingers crossed

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The problem is you are stuck within the training set precision or the abstract nonsense. IT will either detect objects we already know, or it'll just mangle abstractness. The really difficult part is creating either 1) meaningful objects of abstract lines or 2) creating meaningful compositions of trainingset data like dogs and buildings. You have a high chance of creating random combinations rather than precise meaningful ones. The problem is to create knowledge of the world etc

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