Because of this challenge with diverse data, I had to cheat a bit. I’m not directly just taking the output of the Neural Network and adding it to the list of generated cards. There’s a bit of post processing done on the cards, and human selection to eliminate cards that are just clearly nonsense. I realise this is cheating, but just consider the system more of a “Card Ideas Generator”. I’ll make sure to show you some of the raw output as well though.

After the network produces potential cards, I run them through a text processor that checks them for similarities with cards in my dataset. Similar cards are inevitable, but I’m trying to eliminate cards that don’t significantly differ in meaning from cards in the database. A bit of necessary overfitting also means that occasionally the network will just produce an exact copy of a card it’s seen.

Then I run the cards through an automatic spelling corrector. This fixes minor spelling errors that the network made and is still completely automatic, so it’s fair game in my opinion. After this is done, I select the best cards and add them to my list of generated cards.

S HOW ME THE CARDS!

Below is a list 50 white cards I picked out:

a horny forever

a hardworking pile of colon stained shit

an unintentional science at feelings

big bathrooms

low branches

being too late

coat hair

white pudding

secretly tickled meat

a big fat dead hooker

a gay

trying to sex with a large poopy finger

unicorn sticks

not flaws

military decisions

forgetting the supremacy ideas.

bully bumpers

a pile of surprise sex tape.

that dried medical lube

borderline inappropriately hanging out.

your mom’s crush.

a man in a sippy cup

good box

horny white justice.

a burping ballerina

making bread red children.

trump-feeling websites.

clown cock.

going to the monkey bar

not weapons.

a 6-year-old babysitter.

my choke hole in my basement

a range of worn cards

sad penis.

dad’s funny bitch.

my post-travel condoms.

suffer school

holding mom

the sexual vagina that just wanted to communicate

a couple of boards with booze brains

the size of a bulge.

grandma training.

singing “o tannenbaanana.”

the 69%

just a love bag

m’finger bitch

sad standards

buying a virgin on top of the world

something fried

sorry.

The cards that make sense represent about a third of the results the network produced. Admittedly, some of the cards above don’t make sense, but some of the cards that make sense were boring, and part of the fun is creating some nonsensical cards.

Here’s an idea of what the raw (slightly formatted but unmodified) output of my network looks like:

penis.

a shit-to-fie

the contraction of a burrito.

the ring my slowl sorr that tells you with the chinese dogs. public motherwease.

female social suberious with forgetting to parol into a petrodomined body show.

an erection that lasts for hours.

aming down on a game of minivan

the poor

a female orgasm

subway shaving a single panties.

a keyback horse with a tremendous dingus.

an 80-year-old girlfriend

a burger silence

recaulation

zego for one

magic human remains for your secret school. inappropriate labor.

having sex with a dead baby fetus.

(rangta butt pullting people.

being kernels for domester of the mongrey.

a flamboyant man that fires a smiling prince.

a valladian memo baby.

idiots

a somen sort of this with the color off the side to. spending the nuit as every day

smelling sex lives of sexual charases.

getting tape on it.

a doctor with a sopping wet vagina.

a fairy food.

making up for your home.

incurable some sexy tantrum.

buffalo big destroying pop

If you play CAH you might recognize a few of these. “penis”, “idiots”, and “the poor” are all direct copies of cards. And “a female orgasm” is only a slight modification on the card “the female orgasm”. This is the network memorizing some cards, and just spitting them back out. That’s why I analyze the cards for their similarity to other cards in the database before accepting them.

Average number of words in a card was 4.94

You’ll also note that the list of white cards I posted above hardly has any longer cards in it. And the raw output should show you why. The network couldn’t learn to string together longer sentences that made sense. I blame this mainly on low number of long cards in the dataset.

Black Cards (prepare for some garbage)

every successful man is _________.

nobody people think i am a powerful wishing with ___.

the humpty dance is your chance of getting killed by a refugee terrorist.

the new morning, there was _______ and ______.

what do with the sound of ______.

wake up when my husband would say _______

I knew we would be right!

the next people invented ______.

our notorious face of my last position was ________.

_______ is a sure sign of my life.

I could keep going but it’s clear that even the cherry picked best examples of black cards make very little sense. They also tend to be more clearly derived from cards the network has tried to memorize.

Once again, here’s an example of the raw output of the network:

nothing is better the good of death?

check out my new podcast about ______ and ______.

ted cruz caused a still want of ______.

the red room for eating ____ for a kirld and gentler ______. the probilew recommend a trip was completely ruined by ______. ___________. a wedding is a the face.

on cheeristed ______ for his/her dont combrate your signessant-class impressed the only acceptable to have ____ in public.

g go goage ______.

______ is a sure sign of my life.

snatch and ______.

what's the biggest source of tenish in the charge of ______. what do you were a parent when i never have to be a proctive in raid of ______.

momes, my imanager compaint for ______.

i wonder if ______ was all over the poil.

immers is now explore the honors using lose in the country black, ive had a horrible vision, father. i saw mountains crumbling, stars falling from _____.

there can a parent that did you have a moment to talk to my kids and do with ______.

i got have ______.

Conclusion time

Overall, I’d call this project a somewhat interesting/amusing failure. The white cards were often funny but kind of reminded me of one of those quizzes where you use the first letter of your last and first name to create your “hooker name” or whatever. It just shoved words together and hoped it was funny. And let’s not even talk about the black cards. Clearly more examples were needed there.

I’m guessing a closer to “state of the art” network could have done better (no shit), but I’m not sure it would have been THAT much better. I think it would have yielded results that made sense more consistently and copied less, but it’s jokes probably wouldn’t have been better. The problem is that in order to make good original cards, you have to be aware of the culture and things around you. And a network like this one only knows cards that are already created.

The only way I can see of solving this is by training a more advanced network on a broader database of text on many topics, then specializing it for the task of generating cards. But how to do that is currently beyond me.

If you have any ideas of how to do that, or simply better ways to make this task work, please let me know. I’d love to hear your feedback. I’d also love feedback on the post if you have it. I’m still just learning to write these articles and I could use some constructive criticism.

link to deck (whitecards only)