Recurrent Neural Networks (RNN)
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Neural Networks. Deep Learning. TensorFlow. What are these buzzwords? What is the latest Artificial Intelligence craze? This advanced session contains cutting-edge information not easily found online, and does not require a PhD in Machine Learning to understand. Recurrent Neural Networks (RNNs) can be used to generate text that will look like its original training data. There are many articles out there that show hilarious end-results of such adventures, but start-from-scratch walkthroughs that show the raw code are hard to come by.
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Recurrent Neural Networks (RNN)
- 3. ...but hate thinking of a new name?
- 4. Thinking of a video game genre is easy...
- 6. Make it Easy to play with AI Easy to set up Easy to use Easy access to Docker Hub with full guidance Connection to Chat Bot with Node.js Menu | Help | Small talk
- 7. Run and train RNN To Run To pull or run Sarah’s pre-trained Docker snapshot to avoid waiting 8 hours, type: docker pull saelia/rnn-js To Train The way to actually make the RNN generate new Shakespeare text is with the data sampling script: th sample.lua -gpu -1 -checkpoint cv/checkpoint_12900.t7 -length 150 -temperature .7 GPU: Setting the flag gpu to -1 tells the code to train using CPU; otherwise it defaults to GPU 0. Checkpoints: While the model is training, it will periodically write checkpoint files to the cv folder. The frequency with which these checkpoints are written is controlled by the number of iterations, specified with the eval_val_every option. (E.g., if this is 1, then a checkpoint is written every iteration.) Length: An important flag is -length. 100 would generate a body of text 100 characters in length. The default is 2000. Temperature: An important parameter you may want to play with is -temperature, which takes a number in range (0 to 1, 0 not included), default = 1. Lower temperature will cause the model to make more “likely” but more boring and conservative predictions. Higher temperatures cause the model to take more chances and increase diversity of results, but at a cost of more mistakes.
- 8. RNNs: Fascinating but Difficult
- 9. Learned • Docker experience • RNN knowledge • Bot connection • Great names by AI
- 10. Superheroes Designed by Neural Netw ork Speet Stank Red Fart Mister Man Rad Food Sapgirl Woop Ann Man Boomss Boark II Supperman Superbore Slonk Lid Man Green Hooter II Starm Surper Shartar Goons Nana Rider Farm Captain In Redink Wolver Man Wizler http://aiweirdness.com/post/140829108357/superheroes-designed-by-neural-network
- 11. Quincelax • Abilities: Sturdy, Secene Grace • Hidden ability: Tunged Leus Tortabool • Ability: Healy Stream Strangy • Abilities: Wharmwbra, Darp • Hidden ability: Magic Guard Stangute • Ability: Banger • Hidden Ability: Drang Tyrnakine • Ability: Beak Eye Minma • Abilities: Buttery armor, Shell Armor • Hidden ability: Weak armor Pokémon Generated by Neural Network http://aiweirdness.com/post/147834883707/pokemon-generated-by-neural-network
- 12. Recipes at your own risk! http://aiweirdness.com/post/163878889437/try-these-neural-network-generated -recipes-at-your
- 13. Craft beer names, invented by neural net work IPAs • Dang River • Yamquak • Bigly Bomb Session IPA • Binglezard Flack • Earth 2 Sanebus • Tower Of Ergelon • Juicy Dripple IPA • Wicked Geee • Yampy • Widee Banger Fripper IPA Strong Pale Ales • The Great Rebelgion • Thick Back • The Fraggerbar • Dankering • Third Maus • Sip’s The Stunks Belgian • Slambertangeriss • Devil’s Chard • Spore Of Gold • The Oldumbrett’s Ring • Gunder Of Traz • Cherry Boof Cornester • Humple Bobstore Barrel Aged Amber Ales • Snarging Red • Warmel Halce’s Comp Ale • Fire Pipe • Blangelfest • Stoodemfest • Ole Blood Whisk • Frog Trail Ale • Ricias Donkey Brain • Sacky Rover • Gate Rooster • Cramberhand • O’Brien Irish Red • River Smush Hoppy Amber Ale • Rivernillion Amber • Special North Imperial Red • Ambre O’Woo’s Omella Imperial Red Ale Stouts • The Moon • The Bopberry Stout • Cherry Coconut Mint Chocolate Stout • Black Morning • Sir Coffee • Shock State • Take Bean • Single Horde • Whata Stout • Shany Lace • Barrel Aged Chocolate Milksmoke • Shump http://aiweirdness.com/post/163753995072/craft-beer-names-invented-by-neural-network
- 14. Harry Potter and the difference between word-level and character-le vel RNN http://aiweirdness.com/post/164291045392/harry-potter-and-the-word-level-recurrent-neural
- 15. A character-by-character, or “char” model takes one text file as input, and trains an RNN to predict the next character in a sequence. The RNN can then be used to generate text character by character that will look like the original training data.
- 16. New paint colors invented by neural network http://aiweirdness.com/post/160985569682/paint-colors-designed-by-neural-network-part-2
- 17. • The temperature flag makes the most difference. (Expects a number between 0 and 1.) • Changes the novelty and noise is the system, • Creates dramatically different output. • Lower temperatures (e.g. 0.2) makes the RNN more confident, but more conservative • It generates less noise, but less novel results. • Using -temperature 0.2 gives clear English, but includes a lot of repeated words. • Higher temperature makes more interesting/novel output, but more nonsense, misspelle d words • Everything is a trade-off. • Experiment with all settings. Temperature
- 18. Temperature 0.3 and 0.4
- 19. Temperature 0.5 and 0.6
- 20. • There are lots of things that affect how well the algorithm does. Temperature adjusts: • whether the RNN always picks the most likely next character as it’s generating text, or whether it will go with something farther down the list. • Setting the temperature higher or lower can make the algorithm produce a much better output. Temperature 0.7 (my favorite)
- 21. Setup
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- There are lots of things that affect how well the algorithm does. One simple change turns out to be the “temperature” (think: creativity) variable, which adjusts whether the neural network always picks the most likely next character as it’s generating text, or whether it will go with something farther down the list. I had the temperature originally set pretty high, but it turns out that when I turn it down ever so slightly, the algorithm does a lot better. Not only do the names better match the colors, but it begins to reproduce color gradients that must have been in the original dataset all along. Colors tend to be grouped together in these gradients, so it shifts gradually from greens to browns to blues to yellows, etc. and does eventually cover the rainbow, not just beige.