training a gan on bitmaps of words and it's making my eyes hurt (or maybe I just need to go to sleep)

latent space interpolation on a lower resolution version of this model after just 100 batches or so, using matplotlib's default colors because it looks vaguely metroid prime-y

after a few thousand batches of training at a usable resolution on an actual GPU. recognizably "words" now—I wonder if increasing the depth of the model (or the kernel size of the convolutions?) would help it actually learn longer-distance dependencies...

another change I made was having it train on bitmaps of random words weighted by the frequency of the words in a reference corpus (i.e. in this case spaCy's unigram probabilities). the idea was that this would help it learn higher-frequency letter combinations and generate words that mostly replicate the "look" of English in use (rather than words in a word list). the drawback is that it looks like half the latent space is trying to spell out "the"

prototype page layout, sampling each word at random (with the fully trained model, or at least as fully as I care to train it)

nanogenmo, gans 

nanogenmo, gans 

just realized that if I finish this project I'm going to become one of those people that needs to put that diagram of GAN architecture into their slides. I'm going to find myself explaining how GANs work to someone at a party, dear god

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nanogenmo, gans 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo 

nanogenmo 

nanogenmo, gans 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

OCR Output (chars: 1259) 

Co these amlh and warllly vinms of cnee, indeed.

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

nanogenmo, gans, weirdly makes me motion sick? 

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