so napanaginipan ko si Zick yung schoolmate kong co-major ni Joseph slash crush ko … Noon! Hahahaha tapos ayun tinext nya daw ako nagtatanong kung naghahanap ba daw ako ng apartment good for 5person tas mura lang like wtf what was that?! HAHAHAHAHA tas syempre sino to reply ko ganon? Tas kingina sabi niya “crush mo. si Zick” hahahahahahahahahahahahahahaha tapos nagising na ako chineck ko phone ko si bebejoseph nagtext. Hahaha ang sweet ang kyut ang ganda ko 😂 Good morning ❤

anonymous asked:

What would u want to see most in the defenders/daredevil s3??

  • stick dies. elektra kills him.
  • kirsten mcduffie
  • matt punches danny rand in the face a bunch of times 
  • matt goes to therapy 
  • matt spends time with a disabled person who isn’t his childhood abuser 
  • foggy and matt work their shit out, maybe with the help of matt’s new therapist
  • karen and elektra kill criminals together
  • matt’s like “hi i’m matt murdock and i’m autistic” 
  • the entire color palate gets lightened up like 15 degrees 
  • the entire racist plot of the chaste vs the hand is just replaced with literally anything else 
  • stilt-man
  • charlie cox is just suddenly and with no fuss replaced by an actual b/vi actor
New paint colors invented by neural network

So if you’ve ever picked out paint, you know that every infinitesimally different shade of blue, beige, and gray has its own descriptive, attractive name. Tuscan sunrise, blushing pear, Tradewind, etc… There are in fact people who invent these names for a living. But given that the human eye can see millions of distinct colors, sooner or later we’re going to run out of good names. Can AI help?

For this experiment, I gave the neural network a list of about 7,700 Sherwin-Williams paint colors along with their RGB values. (RGB = red, green, and blue color values) Could the neural network learn to invent new paint colors and give them attractive names?

One way I have of checking on the neural network’s progress during training is to ask it to produce some output using the lowest-creativity setting. Then the neural network plays it safe, and we can get an idea of what it has learned for sure.

By the first checkpoint, the neural network has learned to produce valid RGB values - these are colors, all right, and you could technically paint your walls with them. It’s a little farther behind the curve on the names, although it does seem to be attempting a combination of the colors brown, blue, and gray.

By the second checkpoint, the neural network can properly spell green and gray. It doesn’t seem to actually know what color they are, however.

Let’s check in with what the more-creative setting is producing.

…oh, okay.

Later in the training process, the neural network is about as well-trained as it’s going to be (perhaps with different parameters, it could have done a bit better - a lot of neural network training involves choosing the right training parameters). By this point, it’s able to figure out some of the basic colors, like white, red, and grey:

Although not reliably.

In fact, looking at the neural network’s output as a whole, it is evident that:

  1. The neural network really likes brown, beige, and grey.
  2. The neural network has really really bad ideas for paint names.

skirts are cool !!