Dead Poets Soc    ty

AI

RNN - LSTM/GRU Text Generation

source of everything: Martin Gorner, https://goo.gl/zTDm7D

??!!!?!!,,,

EDGAR ALLAN NO

  • Forgot to log accuracy
  • 300k characters
  • 100 iterations
  • 2 GPUs
  • 2 hours?

WHITMAN

  • 60k characters
  • 100 iterations
  • 2 GPUs
  • 15 minutes ???
  • 97% accuracy
  • ???!?!?!

WALT

WILL. I. AM.

SHAKESPEARE

  • Whole night + more
  • 8 CPUs
  • No GPU
  • Martin Gorner's work
  • Complete works

Around and around we go...

RNN? WTF?




#######################
# LE KERAS CODE - WOW #
#######################

# Around 60-ish lines of code
# Around 1000000 headaches

# 40 chars at a time, chars = total no of unique lowercase chars
model.add(LSTM(128, input_shape=(40, len(chars))))

# Softmax layer, just as many neurons as chars then get highest prob
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

Meth

MATH!

TMMDR: Use gates to transform shiite.

Takeaways

  • Applications: speech recog, lang translation, text prediction
  • Improve: Time. RNNs take a lot of computing power and time to train.
  • Wish we had PH data to work with
  • ML is weird. We got 97% accuracy (accdg to Keras) on Walt Whitman. 
  • Clean your data too????!!!???,,,,.
  • ML is hard but fun

Dead Poets SocAIty

By Beato Bongco

Dead Poets SocAIty

Learning RNN by failing.

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