Character-level RNN (Recurrent Neural Net) LSTM (Long Short-Term Memory) implemented in Python 2.7/TensorFlow in order to predict a text based on a given dataset.
Check out corresponding Medium article:
Text Predictor - Generating Rap Lyrics with Recurrent Neural Networks (LSTMs)đ
Heavily influenced by: http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
- Train RNN LSTM on a given dataset (.txt file).
- Predict text based on a trained model.
kanye - Kanye West's discography (332 KB)
darwin - the complete works of Charles Darwin (20 MB)
reuters - a collection of Reuters headlines (95 MB)
war_and_peace - Leo Tolstoy's War and Peace novel (3 MB)
wikipedia - excerpt from English Wikipedia (48 MB)
hackernews - a collection of Hackernews headlines (90 KB)
sherlock - a collection of books with Sherlock Holmes (3 MB)
shakespeare - the complete works of William Shakespeare (4 MB)
tagore - short stories by Rabindranath Tagore (2.6 MB)
Feel free to add new datasets. Just create a folder in the ./data
directory and put an input.txt
file there. Output file along with the training plot will be automatically generated there.
- Clone the repo.
- Go to the project's root folder.
- Install required packages
pip install -r requirements.txt
. python text_predictor.py <dataset>
.
Each dataset were trained with the same hyperparameters.
Hyperparameters
BATCH_SIZE = 32
SEQUENCE_LENGTH = 50
LEARNING_RATE = 0.01
DECAY_RATE = 0.97
HIDDEN_LAYER_SIZE = 256
CELLS_SIZE = 2
Iteration: 0
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Iteration: 500
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Iteration: 1000
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Iteration: 100000
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Iteration: 0
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Iteration: 1000
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Iteration: 100000
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Iteration: 0
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Iteration: 1000
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Iteration: 100000
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Iteration: 0
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Iteration: 1000
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Iteration: 100000
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Iteration: 0
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Iteration: 1000
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Iteration: 100000
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Iteration: 0
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Iteration: 1000
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Iteration: 10000
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Iteration: 231000
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Good morning!
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I'm taking all in my sleep, Im out him and I ain't inspired?
Okay, go you're pastor save being make them
White hit Victure up, it can go down
[Outro: Kanye West]
One time
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Iteration: 0
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Iteration: 511000
āĻ¨āĻž, āĻ¤āĻŦā§ āĻ¯ā§āĻŽāĻ¨ āĻ˛āĻžāĻŦāĻŖā§āĻ¯ āĻĒā§āĻ°āĻžāĻŽāĻ˛āĻž āĻāĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻĻāĻŋāĻ˛, āĻ¤āĻžāĻšāĻžāĻĻā§āĻ° āĻāĻŽāĻ¨ āĻ¸āĻžāĻĻāĻžāĻ¸āĻŋāĻ§āĻž āĻŦāĻ˛āĻŋāĻ˛, 'āĻ¤ā§āĻŽāĻŋ āĻ¤ā§āĻ˛āĻž āĻšāĻžāĻ¸āĻŋ āĻāĻ° āĻā§āĻ āĻā§āĻ˛ā§āĻŽāĻžāĻ¨ā§āĻˇ āĻ¨āĻžāĻāĨ¤'
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āĻ¨ā§āĻ¯āĻžāĻ¯āĻŧ āĻ¸āĻāĻ˛ā§āĻ° āĻ¸āĻžāĻ°ā§āĻāĻ¨ā§ āĻāĻŽāĻžāĻĻā§āĻ° āĻŦāĻžāĻĄāĻŧāĻŋāĻ° āĻ¸ā§āĻā§āĻˇā§āĻŖ āĻĒā§āĻā§āĻ° āĻāĻžāĻāĻžāĻāĻž āĻŦā§āĻ¨ā§āĻ° āĻĒāĻĻāĻ¨āĻžāĻ° āĻāĻĒāĻ° āĻŦāĻšāĻŋāĻ¯āĻŧāĻž āĻ
āĻ¸ā§āĻĨāĻŋāĻ° āĻāĻ°āĻŋāĻ¯āĻŧāĻž āĻĒāĻžāĻāĨ¤ āĻā§āĻāĻ¸ā§āĻŦāĻ˛ā§āĻ¨āĻžāĻĒāĻžāĻ¨āĻā§ āĻāĻŋāĻšā§āĻ¨ āĻ˛āĻāĻ¯āĻŧāĻž āĻĻāĻžāĻāĻŋāĻ° āĻ¨āĻ¨ā§āĻ° āĻŽāĻ§ā§āĻ¯ āĻšāĻāĻ¤ā§ āĻĒāĻ°āĻŦāĻžāĻ° āĻ¸āĻšāĻ¯āĻžāĻ¤ā§āĻ°ā§ āĻŦāĻ˛āĻŋāĻŦāĨ¤ āĻ¨āĻŋāĻā§āĻā§ āĻā§āĻāĻā§ āĻ¨āĻžāĨ¤ āĻāĻ āĻ¤ā§āĻŽāĻžāĻā§ āĻāĻŽāĻžāĻ° āĻŦāĻžāĻĄāĻŧāĻŋāĻ° āĻāĻā§āĻāĻž āĻšāĻ¯āĻŧā§ āĻāĻ ā§āĨ¤
āĻāĻ˛ā§āĻāĻž āĻāĻ°āĻŋāĻŦā§āĻ¨, 'āĻšā§āĻŽāĻĨāĻžāĻ°āĻž āĻ˛āĻā§āĻˇā§āĻ¯ āĻāĻ°ā§ āĻā§āĻ˛āĨ¤ āĻāĻ¤āĻŋāĻŽāĻ§ā§āĻ¯ā§ āĻ¸āĻŽāĻ¸ā§āĻ¤ āĻ¯āĻ¤ā§āĻ¨ā§ āĻŦāĻžāĻšāĻŋāĻ° āĻšāĻāĻ¤ā§ āĻĒāĻ°āĻŋāĻ¤ā§ āĻšāĻžāĻāĻžāĻ° āĻĻā§āĻĒ
Greg (Grzegorz) Surma