For the past week, I’ve been running a port of the Wavenet algorithm to generate poems. A reasonable training result emerges in about 24 hours, — a trained model that can generate immense amounts of text relatively quickly. On a laptop. (Code: github). By reasonable I mean the poems do not have any real sense, no sentient self, no coherent narrative, nor epic structure. But they do have cadence, they do not repeat, new words are plausible, and they have adopted a scattered open line style characteristic of the late twentieth century corpus on which they were trained. Much more lucid than Schwitters’ Ursonate, output is reminiscent of Beckett’s Not I : ranting incandescent perpetual voice.
Remember, these are evolutionary amoebas, toddlers just learning to babble. The amazing thing is that without being given any syntax rules, they are speaking, generating a kind of prototypical glossolalia poem, character by character. Note: models are like wines, idiosyncratic reservoirs, the output of each has a distinct taste, — some have mastered open lines, others mutter densely, many mangle words to make neologisms — each has obsessions. The Wavenet algorithm is analogous to a winery: its processes ensure that all of the models are similar. Tensorflow is the local region; recursive neural nets form the ecosystem. The corpus is the grapes.
Intriguing vintages-models :
Dense intricate Model 33380 — trained with 1024 skip channels and dilation to 1024 (read a txt sample)
the mouth’s fruittiny from carryinga generative cup
Loose uncalibrated Model 13483 with loss = 0.456, (1.436 sec/step) trained on 2016-10-15T20-46-39 with 2048 skip channels and dilation to 256 (read a txt sample)
at night, say, that direction.sleepsnow. so you hear we are shakingfrom the woods
Full results (raw output, unedited txt files from the week of Oct 10-16th 2016) here.
it’s there we brail,beautiful fullleft to wish our autumn was floor
Edited micro poems
…extracted from the debris are here.
through lust,and uptight winking coldblood tree hairsburnedin loss
Python source code + a few trained models, corpus and some sample txt: on github which will be updated with new samples and code as it emerges.
The Model number refers to how many steps it trained for. Skip channels weave material from different contexts. On this corpus, larger skip channels produce more coherent output. Dilations refer to the size of the tensors of the encoder-decoder: eg. [ 1, 2, 4, 8, 16, 32, 64, 128, 256, etc… ] Higher values up to 1024 seem to be of benefit, but take longer to train. Loss is the mathematical calculation of the distance between the goal and the model; it is a measure of how tightly the model fits the topological shape of the corpus; as models are trained, they are supposed to learn to minimize loss; low loss is supposed to be good. For artistic purposes this is questionable (I describe why in see next section). For best results, in general, on this corpus: 10k to 50k steps, 1024 dilations, a skip channel of 512 or more, and (most crucial) loss less than 0.6.
Loss is not everything. An early iteration model with low loss will generate cruft with immense spelling errors. Thousands of runs later, a model with the same loss value will usually produce more sophisticated variations, less errors. So there is more going on inside the system than is captured by the simple metric of loss optimization. Moreover if the system is about to undergo a catastrophic blowout of loss values, during which the loss ceases to descend toward the gradient and exponentially oscillates (this occasionally occurs after approx 60k steps). Generated text from poems just before that (with good loss values below 1.0 or even excellent loss values below 0.6) will produce some ok stuff interspersed with long periods of nonsense or —— repeated **** symbols. These repetitive stretches are symptoms of the imminent collapse. So loss is not everything. Nonsense can be a muse. Mutating small elements, editing, flowing, falling across the suggestive force of words in raw tumult provides a viable medium for finding voice. Continue reading