5038 poems of 16 lines of approx 42 chars each generated in 2h42m

I am now utterly convinced of the impossibility of neural nets ever producing coherent contextually-sensitive verse, yet as language play, and a window into the inherent biases within humans, it is is impeccably intriguing and occasionally nourishing.

Text: Screencast 2018-02-04 18:52:12_PARAGRAPHS.txt.tar

(awd-py36) jhave@jhave-Ubuntu:~/Documents/Github/awd-lstm-lm-master$ python generate_Feb4-2018_PARAGRAPHS.py –cuda –words=1111 –checkpoint=”models/SUBEST4+JACKET2+LYRICS_QRNN-PBT_Dec11_FineTune+Pointer.pt” –model=QRNN –data=’data/dec_rerites_SUBEST4+JACKET2+LYRICS’ –mint=0.75 –maxt=1

Averaged Stochastic Gradient Descent
with Weight Dropped QRNN
Poetry Generation

Trained on 197,923 lines of poetry & pop lyrics.

Library: PyTorch
Mode: QRNN

Embedding size: 400
Hidden Layers: 1550
Batch size: 20
Epoch: 478
Loss: 3.62
Perplexity: 37.16

Temperature range: 0.75 to 1.0

 You disagree and have you to dream. We
 Are the bravest asleep open in the new undead

Text: Screencast 2018-02-03 19:25:34

BDP: Big Data Poetry

Paragraph style: 16 lines of approx 42 chars each : Averaged Stochastic Gradient Descent : Screencast 2018-02-04 18:52:12

I am now utterly convinced of the impossibility of neural nets ever producing coherent contextually-sensitive verse, yet as language play, and a window into the inherent biases within humans, it is is impeccably intriguing and occasionally nourishing.