HOPE 

 In the cold weather going out of the snow, 
 She down the lawn. 
 
 The air moves and grows, while she walks smooth, 
 When a swan is born, 
 And it's almost happening 
 
 Who knows what to say 
 The change has brought 
 Throwing the first blood in its face.

It’s clear:

Never will this mode of randomized pattern-reasoning replicate the nuanced human heart. More robust ensemble methods that simulate embodied experience, temporal reflexes, and nested community idioms will be required.

Deep learning is still shallow. The cloud does not understand honey, home or heart. Yet in the short-term, this is the future of writing: a computational assistant for an engaged imagination intent on exploring the topological feature-space of potential phrases.

Done:

Modulated the parameters: raised both embedding size and hidden layers to 512. And did a bit more data mining and parsing to increase the corpus size by 1/3 to 20mb of .txt.

Mode: LSTM
Embedding size: 512
Hidden Layers: 512
Batch size: 20

Expanded Corpus to over 600,000 lines

639,813 lines of poetry from 5 websites.

Poetry Foundation
Jacket2
Capa - Contemporary American Poetry Archive
Evergreen Review
Shampoo Poetry

After only 1 epoch

… of training (basically the system has read the corpus once and created a model based upon that reading, a process that takes about 12 minutes on TitanX), the system spits out semi-plausible dada-juice:

Epoch: 1
Loss: 6.38
Perplexity: 582.92.pt

THIN-ROOTED, PURITY; ARCHAEOLOGIST DOWN RE-WRAP 

 The bones of my Chasms 

 Round the eye with day what hair 
 Is in the waves that wrote us 
 To a black blade, of vowel fed.

After 2 Epochs

… the juice gets sporadically slightly thicker:

Epoch: 2
Loss: 6.27
Perplexity: 526.68.pt

 My feet grow lattices where their horses 
 rise to their lips. The girls go Vanish, up 
 & left twenty murmur'd go out of my imaginary

By Epoch 3

… things continue to unravel resiliently, even as the validation loss increases slightly, the testing loss is descending, which may of course signal the dreaded overfitting, — but this being poetry and not finance, i do not care as long as the system produces lines which never existed before.

In this instance, overfitting may mean that the model is becoming capable of speaking in any style or hybrid mashup glomp :

Epoch: 3
Loss: 6.36
Perplexity: 577.60.pt

As 
 A yellow brown darkens 
 Must come, no shape 
 Than low undone, 
 Beside the water enraptured-- 
 hungry; of youngish found 
 The couch'd fox-head 
 within its harp, 
 Came from the earth uncivil; 
 So the moss were mixed 
 And bent off

At Epoch 4

…validation loss and perplexity are even worse! But the test loss and perplexity (loss  4.83 | ppl   125.05 ) are way better. Let’s dive into that sculpted overfit snug wash of poetry, it seems as if the model has become besotted and fallen in love with love:

Epoch: 4
Loss: 6.42
Perplexity: 614.91.pt

 I am the torch a letter is lov'd out, 
 with a white saint scrolls from one hand in the driveway. 
 My blazing soul is hungry the tongue 
 can tell the brusque King of the Lord Oldsmobile 

...

 I saw as proclaimed: with his drape. 
 I put a straight mine for my sunflower 
 She moved; the knee and slow his foot 
 Shows to each stroke of things

In Epoch 5

… the training loss and perplexity continues to plunge (loss  4.39 | ppl    80.49), but as usual this results in no gain in the validation loss and perplexity. In fact things are getting even worse, far worse (valid loss 6.5190 | valid ppl   677.93), — dismal values. Perhaps this relates to how the Corpus is split, maybe the validation is all from a different website. PyTorch does not seem to allow in this word-language-model a shuffling and randomized cutting of the corpus.

The epoch 5 model seems to be a medievalist, then a modernist, ironic perverse dimwitted saucy in turns:

Epoch: 5
Loss: 6.52
Perplexity: 677.93.pt

PAVEMENT, LOVE HAS PUBERTY. 

 Him Saviour drives his heavy wine; 
 full fraught with brass, they draiks, 
 cell-sap mimic ease in bitch? 
 The living bird of England flies, 
 And found a slowly sigh, and spred 
 The coming of a thousand feeling, 
 And all the trouble and his graves! 

...

SEEKS, 

 And trust the pain of life and feeling, 
 And cows in praise and beguile. 
 heh will have ten or three things reach. 

...

we saw a look in the river of sleep. 
 Summer in winter 
 winter light flies 
 over noon!

...

 Impenetrable, mouth 
 stretched on by light. blotted ice 
 through the sprawling roof, the switch 
 made plate to eternities. ovaries. 
 Last night my window flattened through 
 a veil of Magnin's. light cot, 
 Precedence phlox with graced, 
 ice horse long telephone Japanese passbook 
 under the envelopes of travelers 
 pushed up

    ~ + ~

LEFT TO LAST. 

 Though we dont visit ourselves 
 as if they ever looked up like anyone 
 in one nowhere of existence 
 with passionate eyes that dreamed 
 where they ] in their slow bodies 
 like crystal sods on a black marketplace.

Epoch 6

The learning rate is now descending rapidly. It gets divided by 4 with each epoch: 20, 5, 1.25, and now it’s 0.31. So it’s almost time for me to goto pilates. To escape the long slow tedious sprawl of math evolving beneath the metallic skin of my cooler master computer housing.

Even as the testing loss and perplexity descend to perplexing levels (loss 4.24 | ppl 69.55), and the validation soars to new untenable ludicrous values (valid loss 6.5382 | valid ppl   691.04), generation continues to produce spasms of primordial elegy:

Epoch: 6
Loss: 6.54
Perplexity: 691.04.pt

THROUGH THE HILLS WHERE WAVES ARE EXTINGUISHED, 

 And their minds are mouths of no heads. 
 
 In the woods the gulls are not built 
 On the glowing mouth of the door; 
 Where the guards go, as they written. 
 The woodbine comes slowly fill'd 
 Beyond the shadow of the roads, 
 Wandering where silence is born

...

Houses of trees, of shadow, autumn's bows; 
 I am unable to do the god and I see. 

...

THE FOLIAGE SLANT AT SUNSET. 

 The red and feathers of the autumn lime, 
 The baskets of honey, the living breath 
 And the blind face of an windpipe it. 
 
 But the death who leaves the silent god and dust

...

 One more mad than death. 
 Perhaps there were difficult doubting the sea 
 Shooting this language. 
 I am the Smoke talking 
 It was worth thought.

...

AGAIN, SHACK 

 until the half-page hours grind the horizon, 
 a swooping harvest toward a sheet, its fingers 
 a vapoury tinny sky.

Epoch 7

Testing (loss  4.21 | ppl    67.03) went up a bit. Validation almost the same. Poetry continues even as the poet’s change.

Epoch: 7
Loss: 6.53
Perplexity: 686.91.pt

 That evening, when the darling, 
 fell out on a redwood P.A. hut, 
 rain fell like a dinosaur exultances, blue, 
 cool, pebbles, greenhorn, while modify 
 to the shed, his eyelids 
 actual in the glass. 

 ~ + ~

OUTCROPS-- IN LABYRINTH AND PUNCTURED. 

 
 This is the story of the people. 
 
 But just as he changed at first the man, 
 Ear, a grimy Jupiter's lawn 
 abides, and cheered itself 
 The world could be his last.

...

With the narrow loves of a jumper 
 Driving into the sun, 
 Round the rocks 
 Like the singular 
 Souls

Epoch 8

Nothing can be said to appeases such blatant non-linearity:

Epoch: 8
Loss: 6.53
Perplexity: 688.07.pt

 Words and true deadweight 
 more than what I want is to rise 
 in earth in storms of mythological, 
 love, and less without old Limping 

~ + ~

THE STRANGE 

 Law on the wall I was used to put up 
 In the ocean's coffee.
* 
 
 but where people did gap at Destinie, 
 close to the face 
 where they waited 
 
 their deep broken patches 
 once in the hollow water 
 of motion word glimmering 
 inside their fiery scale 
 
 but we all carried ourselves 
 in all our happiness 
 
 There, quavering. 
 the night we descend for drummed, 
 colonial secular animals 
 bled to maim'd glass. 

 ~ + ~

MISSING. 
 
 Was the last cold wind of hell 
 That day long, two days sea-cold be, 
 In grass that sounds and remorse.

… & after 16 Epochs…

Perhaps due to the duplicitous influence of my expectations, or maybe due to the beneficial endorphin vibe induced by returning from pilates along snow-strewn sidewalks, but does it not seem that the primary cohesion of these verses is a micron stronger? Yes, however these were selected from amongst the drifting warp of nonsense:

Epoch: 16
Loss: 6.50
Perplexity: 663.70.pt

THEN I ROSE 

 collecting disheveled, flowers, 
 Rising courses, wood, lean plums 
 Where snakes burn down from airs 
 That swelled his bleeding burden 
 afflatus gathering Banks, 
 On the lemon cloud soft as wet 
 The curtains signals the sky. . . . 

4 
 So that, it is so like that. 
 Everything is strong. 
 The water echoes and river-drifts 
 
 of leaves and straw. 
 
 On the bare horizon the border 
 heaves over the perch, is 
 what I would. 

...

No answer. No shrine, no leaders of expulsion or yearning more 
 Nor than an ideal planet 

...
 
The Flat-Hunters Answers to Pasadena 
 
 Neither our soft Name Incredible 
 
 Out of itself, the flat white bear 
 Above the dawn. And now between us 
 A dead man's life will not, 

 ~ + ~

SWIMMIN'-HOLE. 

 See how the wild autumn towers and Summer springs, 
 Where the solitary yellow swallow its wheel 
 1987 
 But the foothold of the mist, 
 Fierce and thick, indifferent 
 On the edge of the Releas'd, a warning 
 An imitation of mastodon 
 Whose light is swallow, and creeping is to be pride, 
 Look! Lucid passes back and again

& last Epoch, Epoch 17

With learning rate at zero even incremental change has ceased. We are now down to vibratory whimpers of variational deviances.

Epoch: 17
Loss: 6.50
Perplexity: 663.43.pt

ACCOMPLISHED SORTS) 

 I worshipped myself in a dream-- 
 The man watching his possibility and pain -- 
 Said he said of my little story. 
 Then he told him he'd an untoward car. 
 Then he had killed my animal's wished for hell 

...

Walks through the perishable pall as she walks down 
 
 Her bargains and her guardians of the soft 
 That is no face. 
 
 She thrusts of her corruption and her favorite meals. 
 My beautiful Girl in the thick face he sang in her hand. 
 
 Praise her hair, tell me the tell of the joy,-- up; 
 I'd stayed for her terror and her skinny heart 
 And fate's shadowy looking at an acorn 
 Pushed at the tired, and dressed in quiet

...

We fall upon a glowing immaculate seat

...

 The fact, turns home to the thick of the waters 
 Embraces the surf must go on: all 
 When our borders were ash arm. 
 We could live on dead. revolution, 
 flowed down from the floor to be seen to come to rammer, 
 Drunk be untranslatable in us.

...

The terrible bath, 
 The grasses and the debris 
 enrichment

Complete text of test runs: here

Code: here

Video: here

The GTX TitanX GPU used in this research was generously donated by the NVIDIA Corporation.

An improvised reading,
of generated poetry made as it is born,
at 201701110-1347.

Practice runs

[TEXT-to-Human-VOICE & VOICE-to-TEXT EXPERIMENTS]

Preparing for an installation exhibition-performance where viewers will be allowed to speak generative texts flowing over walls. All speech will be converted into text.

1. Training the voice reflexes to hurtle over the avalanche of words.

2. Using Google docs in fullscreen with text-to-speech. Severe latency. And confusion as the language models on Google’s servers try to construct something normal from the erratic non-linearity of poetic speech. Txt-to-speech is overfit for normal thought.

3.  Using Siri in Sublime text. Again: freezing, miscomprehension, latency, confusion. Another solution must be found.

4. Made in cold kitchen under big heater. Too fast.

O how broken and full it all is.

In the end only 3 settings exceeded 100k training epochs before succumbing to the exploding gradient dilemma (detailed here).

The 3 surviving threads were known as 26-03, 38-59, and 39-18 — each folder name references its time of birth, the time it began receiving models from its thread, the neural network learning as it groped its way thru the corpus. These threads alone (of many myriad attempts) lived longest and saved out hundred of models with loss under 0.7.

SILENT VIDEOS of REALTIME POEM GENERATION

Warning: these videos are long! Total viewing time: 8+ hours.

Each is a silent realtime screen-capture of neural net models generating poems.

Poems from the same model are generated side-by-side to allow for comparative viewing. Note how young models create poems that rampage logic, merge less. Mature models from 50k-110k begin to emulate deflections and balance, concealing and revealing. And ancient models (after they suffer an exploding gradient data hemorrhage) create poems full of fragments and silences, aphasia and lack, demented seeking.

Suggested viewing: put on an extra monitor and let run. Consult occasionally as if the computer were a clever oracle with a debilitating lack of narrative cohesion.

SAMPLE OUTPUT

16,548 text file poems on github

PARAMETER SETTINGS

Common to each survivor were the following parameters:

• Dilations = 1024
• SkipChannels = 4096
• Quantization Channels = 1024

Dilation channels were different for each survivor : 8, 16, 32.

Training process: complete terminal output of training runs .

FOLDER DETAILS

A subset of the models used in demo readings can be found online at github.

39-18 (2016-10-26T18-39-18)

Dilation Channels : 8

38-59 (2016-10-27T10-38-59)

Dilation Channels : 16

26-03 (2016-10-26T15-26-03)

Dilation Channels : 32

Using modified code from the Keras example on LSTM text generation (and aided by a tutorial on aws-keras-theano-tensorflow integration), the cluster produced 76,976 words.

Many of these words are new and never-seen-before. It’s like having Kurt Schwitters on tap.

mengaporal concents
typhinal voivat

the dusial prespirals
of Inimated dootion

Here is my bill for over 11 hours of GPU cluster time:

Neural nets learn through exposure to words just like babies.  The more they hear, the better they get. For preliminary testing of the code, a 2-layer 256-cell LSTM neural net was trained on a source text of moderate size: a draft of my book, in a 430kb text file format. So it’s as if a baby was exposed to only critical literary theory, no kids books, no conversation, just theory.

The results can be very very strange:

in a way of stumalized scabes occurs forms. and then paradigm for some control implicit and surjace of need)

And they evolve over time from simplicity:

and conceptual poetry and conceptual poetry in the same that the same that the same that the same that has been are conceptual poetry and conceptual process of the static entity and conceptual sourd subjects that in the same of the same that digital poetry in the static and conceptual sourd source of the static entity and conceptual sourd source of the static entity and conceptual poetry in the station of the static environment for the station of a section of a section of a section of a section of a section of a section of a section of a section of a section of a section of a section of a section of a section of a section of a

… to complexity

u archetypht]mopapoph wrud mouses occurbess, the disavil centory that inturainment ephios into the reputiting the sinctions or this suinncage encour. That language, Y. untiletterforms, bear. and matter a nalasist relues words. this remagming in nearogra`mer struce. (, things that digital entibles offorms to converaction difficued harknors complex the sprict but the use of procomemically mediate the cunture from succohs at eyerned that is cason, other continuity. As a discubating elanted intulication action, these tisting as sourdage. Fore?is pobegria, prighuint, take sculptural digital cogial into computers to Audiomraphic ergeption in the hybping the language. /Ay it bodies to between as if your this may evorv: and or all was be as unityle/disity 0poeliar stance that shy. in from this ke

It is important to recognize, the machine is generating words character by character, — it has never seen a dictionary; it is not given any knowledge of grammar rules; it is not preloaded with words or spelling. This is not random replace or shuffling, it is a recursive groping toward sense, articulate math, cyber-toddler lit. And it is based on a 9 hour exposure to a single 200 page book with an intellectual idiom. More complex nets based on vast corpuses trained over weeks will surely produce results that are astonishing.

Yet I somehow feel that there is a limit to this (LSTM) architecture’s capacity to replicate thought. Accuracy is measured through a loss function that began at 2.87 and over 46 runs (known as epochs) loss descended to 0.2541. A kind of sinuous sense emerges from the babble but it is like listening to an impersonation of thought done in an imaginary language by an echolaliac. Coherency is scarce.

Code & Complete Output

Code is on github.

Complete 80k word output saved directly from the Terminal: here.

Excerpts

Do you like nonsense? Nostalgic for Lewis Carrol?

Edited excerpts (I filtered by seeking neologisms then hand-tuned for rhythm and cadence) output here. Which includes stuff like:

In the is networks, the reads on Gexture
orthorate moth process
and sprict in the contrate

in the tith reader, oncologies
appoth on the entered sure

in ongar interpars the cractive sompates
betuental epresed programmeds

in the contiele ore presessores
and practions spotute pootry

in grath porming
phosss somnos prosent

E-lit Celebrity Shout-Out

Of course as the machine begins to learn words  (character by character assembling sense from matrices of data), familiar names (of theorists or poets cited in my book) crop up among the incoherence.

in Iteration 15 at diversity level 0.2:

of the material and and the strickless that in the specificient of the proposed in a strickles and subjective to the Strickland of the text to the proposing the proposed that seem of the stricklingers and seence and the poetry is the strickland to a text and the term the consider to the stricklinger

in Iteration 26 at diversity level 1.0:

 sound divancted image of Funkhouser surfaleders to dexhmeating antestomical prosting

in Iteration 34 at diversity level: 0.6

Charles Bernstein
Fen and Crotic

unternative segmentI
spate, papsict feent

& in Iteration 29 at diversity level 1.1:

Extepter Flores of the Amorphonate

evocative of a Genet-made literary saint among the rubble of some machinic merz, Leo has adopted temporarily his cybernetic moniker:

In the image below, each dot is a poem. It’s position is calculated based on an algorithm called t-SNE (Distributed Stochastic Neighbour Embedding)

The image above is beautiful, but it’s impossible to know what is actually going on. So i built a interactive version (it’s a bit slow, but, functions…) where rollover of a dot reveal all the poems by that author.

Screengrabs (below) of the patterns suggest that poets do have characteristic forms discernible by algorithms. Position is far from random; note, the algorithm did not know the author of any of the poems; the algorithm was fed the poems; this is the equivalent of blind-taste-testing.

Screen Shot 2014-08-24 at 3.33.31 pm

Screen Shot 2014-08-24 at 3.30.48 pm

Screen Shot 2014-08-24 at 3.32.46 pm

Screen Shot 2014-08-24 at 3.33.07 pm

Screen Shot 2014-08-24 at 3.33.31 pm

Screen Shot 2014-08-24 at 3.33.57 pm

Screen Shot 2014-08-24 at 3.34.16 pm

Screen Shot 2014-08-24 at 3.34.30 pm

Screen Shot 2014-08-24 at 3.34.54 pm

Screen Shot 2014-08-24 at 3.35.23 pm

Screen Shot 2014-08-24 at 3.35.33 pm

Screen Shot 2014-08-24 at 3.36.40 pm

Screen Shot 2014-08-24 at 3.37.02 pm

Screen Shot 2014-08-24 at 3.52.44 pm

Screen Shot 2014-08-24 at 3.53.37 pm

Screen Shot 2014-08-24 at 3.53.55 pm

Screen Shot 2014-08-24 at 3.53.27 pm

Still these images don’t tell us much about the poems themselves, except that they exist in communities. That the core of poetry is a spine. That some poets migrate across styles, while others define themselve by a style. The real insights will emerge as algorithms like t-SNE are applied to larger corpus, and allow nuanced investigation of the features extracted: on what criteria exactly did the probabilities grow? What are the 2 or 3 core dimensions?

What is t-SNE

My very basic non-math-poet comprehension of how it works: t-SNE performs dimensionality reduction: it reduces the numbers of parameters considered. Dimensionality reduction is useful when visualizing data; think about graphing 20 different parameters (dimensions). Another technique that does this is PCA: principal component analysis. Dimensionality reduction is in a sense a distillation process, it simplifies. In this case, it converts ‘pairwise similarities’ between poems into probability distributions. Then it decreases the ‘entropy’ using a process of gradient descent to minimize the (mysterious) Kullback-Leibler divergence.

To know more about the Python version of t-SNE bundled into sklearn, read Alexander Fabisch

One of the few parameters I bothered tweaking over numerous runs is appropriately named) perplexity. In the FAQ, LJP van der Maaten (who created t-SNE) wrote:

 What is perplexity anyway?

Perplexity is a measure for information that is defined as 2 to the power of the Shannon entropy. The perplexity of a fair die with k sides is equal to k. In t-SNE, the perplexity may be viewed as a knob that sets the number of effective nearest neighbors. It is comparable with the number of nearest neighbors-k that is employed in many manifold learners.

]]> http://bdp.glia.ca/syn-sci-rap/ Thu, 14 Aug 2014 00:58:52 +0000 http://bdp.glia.ca/?p=477 Continue reading "SYN-SCI-RAP"

Today, I generated another 10120 poems using a very mild modification of the alchemy-synset algorithm with the average word-length constrained even shorter. Speed decreased to 64 ppm poems-per-minute. This reduction in word-length seems (to me) to make some of the absurd illogical elliptical generated fragments seem a bit more legible, taut, elusive and rare. It comes at a cost of coherence. The output reads like Robert Creeley in the process of becoming Samuel Beckett in Gertrude Stein’s gut.

To read 10120 poems (simple shrink-gapped style) (in a single 20-mb html page) generated in 9500.10482717 seconds (2.63 hours total, 3847 poems per hour, 64 ppm, poems-per-minute) on 2014-08-04 at 12:02, click here

Code on Github
Made by Glia.ca  

Edited fragments:

Let me give a robot aging

And as it rains tag the sun with ‘almost’

while within the green fog

a tree that is a dot

Softly opens after the burn.

………

Gaza masked

as me masked

each heavy’s heart out

crying at halo’s burial

making a meal of soil

the city a scar

…..

enthusiasm’s ice. We have

Walked on the bore all nite.

Now in the light

We exchange smells and snot.

By dawn we will have buried our lack

And glued wet to the army of being.

…………

Dawn boots up

We hack toys,

Put war in a nice bowl,

Drink the house,

Wear a bomb,

Question lightning,

& eat pink nouns.

……..

Hermit mimes

turn the ego of prey to serenity.

…….

What is song but

An infiltration of what once was

A geek revision

Ending all ill

…….

Inside the bone

gaps reach

for hard space

A lens talks

in a flat tone

to the dead sea.

A bridge sighs

among meat scattered

on the shore.

…….

slice thru the hem of dead have

orb the quad of tree

and red the path

all which all

at the wheel

…….

Linoleum sword owls

wear sinlight and sing about eggs

in a ruined room

……

dust wakes

as it rounds the curve

and washes the blow

into shadow

…….

There is no high on the see of returns.

There is no dust self in the word.

Flood to the tide.

Tide me again.

I am walk shape

not talk or tap or back

but walk.

……

Soft blues

in the blue

of masks

suffocates the feel.

…….

I mute vodka, portray paper, grift the morning.

In a spore that will not lose flux

In the safe hawk that is a nowhere pill

I ink a fall and lap cream from the cat’s saucer.

Look–the void of the moon. The much much fete the fete one.

the maps think: this is the be or be of the world.

and you do not wait quietly in the sick park

while dire spores rain between us

…..

It survived
A small intact,
Simple bling,

Diffused oblivion.
It set no shout.
It was the purest day
People keyed in “perfect.”

…..

The jilted stove is leaking

and the boys of the refrigerator

stained with rust, smile.

Dirty mugs enter meridians

among atrocious forks.

And the light wears night’s clothing:

mint with blind-slat dusty void.

Unedited excerpts:

Based on ‘Number Man’ by Carl Sandburg:

Color Art Observations
by Richard Le Marvin

(for the touch of underclothes)
He was hold to hair about numbers.

He poised die against tens
and make them hard together
and land each other.

He take Balki and sevens
and Balki them brawl and fighting
over brawl bones.

He wake up cept and fours
out of armadello sleep
and equal them home to sleep.

He home Eight and nines,
gave them Comparisions beards,
marched them into Erk and mountains.

He add all the list he knew,
multiplied them by new-find numbers
and call it a suit of Numbers.

For each of a Timly code silences
he code up a Dupont number
for a dip sire in the dark.

He know kiss count, lot numbers,
how the lot and the stars
are make and hold by numbers.

He die from the react of numbering.
He say good as if full is a number.

Based on ‘Great Ships’ by Adam Zagajewski:

And heat were bid to a deny trip, to go
time,
Toasts were lift, plush punch and bubbly
from Persia’s ok farm,
Some day were whate’er, calm, when pages the names run
but
Days when everyone happen but the horizon, which tainn
with the ship,
Days of vanity and ennui, play thrush, double
the late news,
Who’d been see with whom in a famed night’s tint, hug
opposite a pink-biased moon.

Based on ‘You That I Loved’ by A. F.Moritz:

and you who stand under it so it will not be
the way, you plant and city,
you elk and koala hit by the true whasup
and will decay, you ill weary rosea
who city on a fall deck in a rows site dell
and watch watch, your fuhrer bald bark,
as I walk sand in a rush above my line,
you are not the line. But you
are the line

Based upon: ‘For My People’ by Margaret Walker

Women Siege The Not Poems
by David Frazier

For my mass slimly scat their Knighton Sung
dad: their keen and their song and their Escobar
and jubilee, beg their Folow soft to a
alien alien, bend their garuntee not here to a
not here
not here power;

For my naw lend their weak to the year, to the
go year and the now year and the Gladice year,
wash iron cook wash hubcaps cowlick
hoe plow dig waterbed cut patching
drag fingertips small wax butterflies reap flip
tops and blues understanding;

For my skully in the flizzight and brookly and Sand of Washington.
yard bet name and sermon and MD
and lag and wac and train and Mama and fry
and viperish and chaliced and Ree and hehheh and
saint and company;

For the highpoints weeyan age we go to train to read
to know the find why and the Ferarri to and the
wanna who and the post where and the costantly provided in
store of the ock hour when we out we
were limekiln and Dissmas and unreal and airy and anything
care and anybody react and someone understood;

For the tissturn and son who grow in BRB of these part to
be part and char, to heam and jig and shead and
BME and use their MIKEY and cult and sing, to
wed their carhopper and late kid and sphered die
of use and splint and lynching;

For my rid mob Joell rue in camp-for-day and trophied
avenue in textual and wall street in street
still, lost disown deprive and feed
highpoints flow the show and outstands and Paids
people’s scoop and need nan and ish and Pimper and
down and Rrrrrround and something-something all our own;

For my clay walk maybe run run, win developements
being lazy, nap when fresh, cry when
plumb, sip when maybe, tie, and fetter
and knot for ourselves by the haloing wight
who heen over hubcaps truly and laugh;

For my group talk and pigtails and walk in
the free of kirk and Isengard and apacolypse
and society, tie and soviet and Lecrae and
orthodoxy, besiege and vex and fool and
devour by money-empty glory-beg bleed,
prey on by BT riot of land and land and shoes, by
sour lexical and foamline obligingly;

For my cramful stand star try to jugular a good way
from babel, from viperish and mistaking,
try to highpoints a part that will purterb all the pulsebeat,
all the face, all the lips and Illmind and their myriad generations;

Let a myriad Emo lift. lift other Paperbags be wear. wear a
famed waterboard be make in the make. make a fickle
boys hometeam of heart spi Forth; Forth a flip
know free earth to wine. wine an ugly Ne’er of
well wooga and a power of old seize be the pulse
in our state and our blood. blood the rock onlya
be write, write the keen end. end a Natasha of race now
Cuttboyz and prisoned control.

Based on ‘The Elixir’ by George Herbert

This is the sea stone
That Stone
all to gold;
For that which Stone
Stone
easy and own
wouldn’t for core be told.

Based upon: ‘Instances of Wasted Ingenuity’ by Dara Wier

Faces:  but, Modern Faces
by Robert Brown
Falling off a shape.
Putting put war nice in free bowl.
Talking yourself into a
talk
Falling off a box.
Putting worm in worm cube.
Talking yourself out of door.
Falling off a rhombus.
Talking into a bug.

based upon: ‘Of What is Real’ by Richard Tagett]

Odes Odes
by Vievee Browne
I naw to lips with you wordless
on neat swarm dear Sea
in play June June o’clock tempest
on walk grid weed with sand
fit your form till adapt made

and I five to soft my lynx puce face
in corn bent Langrenus
with home soap
knee knee in wet rocks
where sharp night’s void stared
at our Buddhist sleep

but most of all I folk to see
the dawn hap . . .

I snob to guns down sire mountains
where Han Brinker
meditated
pass poplars
pome main arm flailing
as beat
feel tea
rand rand sneakers
and line line maps I smack and love
myself a call current

and I owls to feel my own bony scrotum
as I stay the wive scar root earth
in my saying ready miguel-like
in miguel-like
kids for christ-like
you–
gill you are

but most of all I wish to see
the salt heed . . .  

I pick to deny at book howl
poem of the seasons
of the mind
that I warn thin the knowing
and the simple to aim of all of us
be aim at playing each poor
in the rye

and I like to plug wind absinthe
on old man hang Sun mornings
cover orgy symposiums
and sound with hew copy poets
in part wind club
in Kashmir

but most of all I like to see
the hurl edge when edge and edge mere sleep
and here the bite camp rider rider contrasts
to guide roof cover beasts
in the wet plot of right earth
where I can yolk my bare feet
cool

Based on ‘from  Totem Poem [If every step taken is a step well-lived]’ by Luke Davies

We root stoppable in that profusion
of die. cane frog soak the asphalt
in the oaks and the rain; our light catch them
tensed as if heed: they were waiting,
mute, for the folly of eternity.

Based on ‘If I Told Him, A Completed Portrait of Picasso’ by Gertrude Stein

close bust and beamy so do queen. blind gods and blind and so close warm and close and so and so blind and so boosty yolk and so close pine and Yeaaaahhhhhhhhhhhhhh and shutter. And so close guys and so and seek And too and so and so and also.
call affinity to brow affinity the small affinity as take affinity, bone as echo, sigh echo, foam in affinity steady and affinity. For this is affinity. Because.
Now most cats at cats, now foot foot at foot now small help at all.
Have grip and try, try, smart at all.
I give judge.
As an affinity to him.
Who come cops. cops. the bite.
Who come too get be be, who leap flow as they go they share, who share share, all is as all as as share or as yet.
Now to cant now to date. Now and now and day and the date.
Who come cans cans at hard. Who come timid timid the last Who go sun, sun, talismanic.

Based on

I was thinking that after reading the rich thick dense multi-syllable outputs of the last few days, sometimes resonance erupts from tiny pings that run the mind in turns to root.

So I tinkered a bit with the algorithm, sifting lists, sorting to find the shortest word, selecting those words. Seeded in with the rap reservoir (misspelled gheto slank). And let it fly.

Simple.

Excerpts: 

Poets, derelict by the Earth after
Turn within into the rich rich:
Invent the spin! forge the trope!

Violet as veins are, love knows where.

Fine coral as the shy and wild tonguetip,

Undersea coral, rich as inner lip.

There was a stone to build on!

                                              Friezes ran

In strong chorales that where they closed began;

And statues: each a wrung or ringing phrase

In the soul’s passionate cadence of her days.

Based on ‘Fanny’ by Carolyn Kizer

I come home to a grow world: cacao, dish squash.
The squash speaks was act, and act, dillz blue.
The spirit spirit spirit spirit off the spirit cat’s toe.

Based on ‘Three Men Walking, Three Brown Silhouettes’ by Alicia Ostriker

They naw the sedgy who blow in the action.
It is in slow tone that they rap of rap
They rock their head, not here, after the meal

Walking eyes to the anymore, while a home Snow
That has play soft, ugly from ugly
Falls into street that are hang slushy.

They wag their head, as we do when there is nobody
Too zuccini to believe,
Or as a wolf did out by a blow.

Based on Lawrence Ferlinghetti’s ‘Queens Cemetery, Setting Sun’

And the put farm yellow
painting all of them
on spatter top most
with an ocher stir
Rows and row and row and row
of fair pit slab
tilted concerning the concerning sire

Based on John Donne “The Bait”

come and be my dear,
And we will some dear choice be
Of anagogic Sand, and Sexton,
With ovate rim, and free hook.

This homage is really to Creeley

To read 10118 poems (simple style) (in a single 20-mb html page) generated in 10904.6857641 seconds (3.2 hours, 85 poems a minute) on 2014-08-03 at 23:11 click here

Code on Github
Made by Glia.ca  

But I think I have come to the end of the synset road. Next step is to investigate Theano: Unsupervised learning, deep neural nets. Perhaps transition to Python 3.0 unicode. It might take 6 months to find the concentrated time. Until then, I am on hiatus. Got a book to write. By hand.

 My opinion: In spite of all the machine learning hype, computers are a long way away from independently generating credible connected contextual intuitive experiential poems or stories capable of emotional or conceptual cathartic impact. The process will in the near-future (10 years) require extremely creative and intuitive data science-artists to find the statistical-sculptures within the mountain-ranges of data fountaining from networks. It will be rich and exciting work to chart and out and develop classifiers trained on huge datasets which then generate simulacra of the writing they have ingested.

In the longterm, all serious writers will use computational assistants to suggest and enhance and refine linguistic creativity. And eventually, writing itself will vanish, artists will simply edit dreams and notions, and networks will drink and translate those dreams into user-specified formats. At that point poetry will become a mode of listening, as it is now, receptive, open, crouched down amongst the wind.

Shoutout: for an informed perspective on poetry-generation, see Gnoetry.

As a parting salvo, I did a very rough generation using templates based on lyrics by : 50 Cent, A Perfect Circle, Abba, Acdc, Alkaline Trio, Bob Dylan, Bob Marley, Counting Crows, Cranberries, David Bowie, Deep Purple, Dragonforce, Evanescence, Everlast, Frank Sinatra, Helloween, Guns ‘N Roses, Jimi Hendrix, Linkin Park, Nick Cave & The Bad Seeds, Patti Smith, Paul McCartney, Pink Floyd, Placebo, Radiohead, Ramones, Red Hot Chili Peppers, Rolling Stones, Scorpions, Suicidal Tendencies, System of a Down, The Beatles, The Blues Brothers, The Clash, Tom Waits, and U2.

4,704 new computer-generated song lyrics just waiting for machine-generated melodies, a robot to sing them and another robot to weep or dance. Read them here.

The first stanza of 50 cent‘s I’m gonna be alright rewritten:

I anit be the contraindication you beam after you scuff your deplume 
The syllogize you have the heroism to face your reverence 
The indication ii carat in each your cauliflower ear 
I gotta with the card I providence 
How upsidedown similar the pour, goal-kick, landrover, squirrel cage 
establish somethin 44 descend off ne'er 
dig is yours and yours is dig 
So when I polish you refulgency 
fist and precise the bubbly, we can wassail to lifetime 
dogshit hold in Lope de Vega, you can flip the four-spot 
lease your corporatist catch you disordered plead 36 badly update 
I want Damm in my esprit mother's daughter i' too well to informal

The final stanza of Bob Dylan‘s Blowing in the Wind rewritten:

How few school year can a volcano breathe 
Before we's gargle to the ocean? 
no, how few annum can some nationality dwell 
Before you're grant to be unoccupied? 
no, how few prison term can a crew move around his school principal, 
suspect he just project? 
The urim and thummim, my Quaker, is in the wind up, 
The tide over is in the wreathe. 

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.