This activity is based on the aboriginal sketch-rnn demo, and is a angle of sketch-rnn-js, but customized for dicks.
The alignment is declared in this paper: https://arxiv.org/abs/1704.03477
Dataset acclimated for training is based on Quickdraw-appendix.
“Mean Time To Dick is a key admeasurement of any accustomed animal or apparatus intelligence system.” — Elon Musk, on dick-rnn.
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Articles about dick-rnn about the world, in The Next Web, Boing Boing, PC Gamer, Mashable, Dlisted, 9GAG, Código Espagueti and HD Tecnologia (Spanish), Feber (Swedish), dobreprogramy (Polish), futuretech (Austrian German), 4Gamers (Traditional Chinese), Gigazine and Karapaia (Japanese).
From Studio Moniker’s Quickdraw-appendix project:
In 2018 Google open-sourced the Quickdraw abstracts set. “The world’s better doodling abstracts set”. The set consists of 345 categories and over 50 actor drawings. For accessible affidavit the abstracts set was missing a few specific categories that bodies assume to adore drawing. This fabricated us at Moniker anticipate about the moral absoluteness big tech companies are arty on our all-around association and that best bodies agreeably acquire this. Therefore we absitively to broadcast an addendum to the Google Quickdraw abstracts set.
I additionally accept that “Doodling a penis is a animated attribute for a alienated act” and additionally “think our moral compasses should not be in the calmly of big tech”.
Main Dick Demo
Predict Multiple Dicks
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Predict Single Dick
Simple Dick Demo
The dicks are anchored in the concern cord afterwards share.html.
Examples of sharable generated dick doodles:
This alternate neural arrangement was accomplished on a dataset of almost 10,000 dick doodles.
The Quickdraw-appendix dataset was candy via incremental RDP epsilons to fit best dicks aural 200 steps. Note that I acclimated the raw version, not their simplified version, back the dicks were added detailed. The candy dataset that is compatable with sketch-rnn’s acclamation (no pun) is in this repo as dataset/dicks.npz and can be loaded this way:
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Training samples from the dataset:
For best results, alternation with absence sketch-rnn settings, but use a dropout accumulate anticipation of 80%. Early endlessly was performed on the validation set. To aerate samples acclimated for training/validation, no analysis set is used, and the analysis set is aloof set to the aforementioned 496 validation samples to be compatable with the abstracts architecture accepted by the absolute code.
Command acclimated to alternation the TensorFlow sketch-rnn model:
I begin a Jupyter anthology in the sketch-rnn repo that calmly adapted the TensorFlow checkpoint into the .gen.full.json architecture that sketch-rnn-js can use, with the command:
Update (4/27/2020) The Quickdraw-appendix dataset was updated, and there are now 25K examples, up from the beforehand 10K. I candy the newer dataset as dicksv2.npz with a able train/valid/test breach of 23000/1500/693 samples. The Main Audience has been adapted to use a larger, but hardly slower archetypal accomplished on the revised dataset absolute added training examples.
Original authorization for anniversary book is adumbrated in the attack animadversion for anniversary antecedent file, or referenced in the URL declared in the antecedent file. Creative Commons Authorization for datasets additionally adumbrated accordingly.
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MIT Authorization for my specific added work.
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