All Projects â†’ gsurma â†’ Text_predictor

gsurma / Text_predictor

Licence: mit
Char-level RNN LSTM text generator📄.

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python
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Text Predictor

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)📄


Heavily influenced by: http://karpathy.github.io/2015/05/21/rnn-effectiveness/.

Idea

  1. Train RNN LSTM on a given dataset (.txt file).
  2. Predict text based on a trained model.

Datasets

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.

Usage

  1. Clone the repo.
  2. Go to the project's root folder.
  3. Install required packagespip install -r requirements.txt.
  4. python text_predictor.py <dataset>.

Results

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

Sherlock

Iteration: 0

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Iteration: 500

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     Book into here, but I told at ht it something do was sack knet afminture-ly. We moke, do oR before drinessast farm. I

Iteration: 1000

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Iteration: 100000

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 details grieves of his east back. The week shook this strength.
 There was no mystery for y

Hackernews

Iteration: 0

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Iteration: 500

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Microsign Sy Scodwars
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Trase elacts Macasications Data Freit Paily trigha bourni

Iteration: 1000

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Tx-: IPGS
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Track.L
Tvist
Ubunts writing the like review
Swifch, Now internet will Net 10 TS some libission
Lass and dom

Iteration: 100000

More Than 10 Years Old Available for Free Lens
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Shot-sizzations of catV; Google - Way Domer Sacks Not Auto-accounts
Amit Gupta needs is moving

Shakespeare

Iteration: 0

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Iteration: 500

ticlother them his steaks? whom father-ple plaise't!

HORATIO:

GLOILUS:
Le wime heast,
'Tind soul a bear if thy Gulithes? Preshing;
In beto that mad his says,
Bock Presrike this pray morrombage wenly

Iteration: 1000

HENI:
If which fout in must likest part sors and merr'd?
E sin even and mel full and gooder?

BRUTUS:
Heno Egison to a puenbiloot vieter.

DROMIO OF SYRACUSE:
That is
never standshruced meledder morng

Iteration: 100000

Be feast, tent?

LYSANDER:
And thou love so kiss, to dipate.

All Cornasiers of Atheniansiage are to my sake; but where in end.

APEMANTUS:
Did such a pays. Go, we'll proof.

BERTRAM:
I am reason'dst 

War and Peace

Iteration: 0

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Iteration: 500

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Iteration: 1000

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sonison 

Iteration: 100000

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tactfully replied that Dolokhov
crossing her to them. He looked his face in snow face, but sound at closely deigning dogron (for Germans: "We le

Darwin

Iteration: 0

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Iteration: 500

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Iteration: 1000

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Iteration: 100000

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Kanye

Iteration: 0

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Iteration: 1000

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Youce, I and fleassy is

Iteration: 10000

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Stand American, no more
Yeah my Benz,.AD and brosi?
Cause you'll take me, breaks to the good I'll never said, ""I met her bitch's pussy is a proll ...
WHO WILL Say everything
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(Stop that religious and the hegasn of me, steps dead)
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Tellin' it and sales there
Got a niggas ass a lots over?
So I clay messin 6 wrong baby
Dog, we lose, ""Can't say how I'm heren

Iteration: 231000

right here, history on you
Dees so can do now, sippin' with niggas want to go

[Hook]
Good morning!
He wanna kend care helped all wingâ€Ļ the live, man
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
To make them other you're like Common
A lit it, I'mma bridgeidenace before the most high
Ugh! we get much higher

Tagore

Iteration: 0

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āĻšāĻ‡āĻ¯āĻŧā§‡āĻ›ā§‡āĨ¤ āĻ•āĻŋāĻ¨ā§āĻ¤ā§ āĻĻā§ƒāĻˇā§āĻŸāĻžāĻ° āĻ•ā§āĻŖā§āĻĄāĻ¨āĻŋāĻŦ āĻ¯āĻžāĻ‡āĻˇā§‡āĻ° āĻĻāĻŋāĻĻāĻŋ, āĻ…āĻ¸āĻŽā§āĻšāĻŋāĻŖā§āĻ¯ āĻ†āĻŽāĻŋ āĻŦāĻžāĻ˛āĻ•āĻžāĻ˛ā§āĻ¯ āĻ¸āĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻĒāĻĄāĻŧāĻŋāĻ˛ā§‡āĻ¨āĨ¤
āĻšāĻžāĻ°ā§‡ āĻŽāĻžāĻ āĻŋāĻ°āĻĒāĻŽā§āĻŦāĻ¤ā§€āĻ° āĻ•ā§āĻˇāĻŖ āĻ¨āĻžāĨ¤ āĻ­ā§ŽāĻ•ā§āĻˇāĻŖ āĻšāĻ¯āĻŧ, āĻŦāĻ˛āĻŋāĻ˛, "āĻ¸āĻ¤ā§āĻ¯; āĻ…āĻ¨ā§‡āĻ• āĻ¨āĻŦā§€āĻ°ā§āĻžāĻžāĻ¸āĻž āĻ¤āĻžāĻšāĻžāĻ° āĻāĻ•āĻŸāĻŋ āĻ…āĻ°āĻŖā§āĻĄā§āĻ¯ā§‡āĻŸāĻžāĻ°ā§€āĻ° āĻĨāĻŋāĻļ āĻšāĻ‡āĻ¯āĻŧāĻž āĻ†āĻŽāĻŋ āĻšāĻžāĻ°-āĻĒāĻĨ āĻŦāĻ°ā§āĻŽā§‡āĻ° āĻĒāĻĨāĻž āĻĒāĻĄāĻŧāĻŋāĻ˛ āĻĻā§‡āĻ–āĻžāĻ¨ā§‡āĻ‡ āĻ‰āĻ āĻŋāĻ˛āĨ¤ āĻ¨āĻž āĻ¸ā§‡āĻ‡āĻœāĻ¨ā§āĻ¯ āĻšāĻ¨ā§āĻŽā§‡āĻ° āĻ­āĻžāĻ‡, āĻāĻ•āĻĒā§āĻ°āĻžāĻŽ āĻšāĻ‡āĻ¯āĻŧāĻž āĻ–ā§‡āĻ˛āĻž āĻāĻŦāĻ‚ āĻŽāĻ¤ā§‹ āĻœāĻžāĻ¨āĻžāĻ‡āĻ¯āĻŧāĻž āĻŽāĻšāĻžāĻ° 		āĻŦāĻ¨ā§āĻ§ āĻ›āĻŋāĻ˛āĨ¤ āĻ¸āĻŋāĻ–āĻŦāĨ¤'
āĻŽāĻ¨ā§‡ āĻ¤āĻžāĻ° āĻŦāĻžāĻ˛āĻŋāĻ˛ā§‡āĻ¨, āĻŦāĻžāĻ­ā§€āĻ° āĻ†āĻŽāĻžāĻ°āĻ•āĻžāĻ° āĻœā§āĻ¯āĻžāĻĨ āĻ•ā§āĻ˛ āĻļā§‹āĻ• āĻĒāĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻ¤āĻžāĻšāĻžāĻ•ā§‡ āĻ¨āĻŋāĻƒāĻļā§‡āĻˇ āĻ•āĻ°āĻŋāĻ¯āĻŧāĻž āĻ āĻŋāĻļāĻŋ āĻ†āĻ° āĻ–āĻŦāĻ°āĻŖ āĻĨāĻžāĻ•āĻŋāĻŦāĻ¤āĻžāĻ° āĻ¸āĻ™ā§āĻ—ā§‡ āĻļāĻŋāĻ­āĻŋāĻŦāĻžāĻ° āĻ¯āĻĨāĻžāĻ˛ā§‚āĻĒā§āĻ¤āĻžāĻ°ā§āĻ¯ āĻŦāĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻžāĻ“ āĻ›ā§‡āĻ˛ā§‡āĻˇāĻŦāĻžāĻŦā§āĻ° āĻ¨ā§‚āĻœāĻŋāĻ¯āĻŧāĻžāĻ°āĻž āĻļā§āĻ¨āĻŋāĻ¤ā§‡ āĻ āĻžāĻˇ āĻšāĻ°āĻŖ āĻĢāĻžāĻĄāĻŧāĻŋ āĻĢā§‡āĻ˛āĻŋāĻ¯āĻŧā§‡ āĻ›āĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻ¤ā§‹ āĻšāĻ¤ āĻ•āĻĒāĻŋ āĻāĻŽāĻ¨ 

Iteration: 10000

, n nthee tin-āĻāĻ•āĻŸāĻŋ āĻ¸āĻŦā§āĻƒāĻĄā§‡āĻļāĻ¨āĻĒāĻĻā§‡ āĻ†āĻŽāĻŋ āĻ¯āĻ–āĻ¨ āĻļā§‡āĻˇ āĻŦāĻŋāĻļā§āĻŦāĻžāĻ¸ āĻĻāĻŋāĻŦāĻžāĻ°āĻŋ āĻ¸āĻžāĻĻāĻž āĻ‰ā§ŽāĻ•āĻŸ āĻ…āĻŽāĻŋāĻ¯āĻŧāĻžāĻ° āĻ•āĻŖā§āĻ ā§‡ āĻļā§āĻ¨āĻŋāĻ¤ā§‡āĻ¨, 'āĻĻā§‹āĻ•āĻž, āĻ¸ā§āĻŦāĻ•ā§āĻˇā§‡āĻŸ āĻĻā§āĻ‡-āĻāĻ•āĻŦāĻžāĻ° āĻŽā§‚āĻ°ā§āĻ›ā§‡āĻ° āĻ‰āĻĒāĻ°āĻ‡ āĻ¯āĻ–āĻ¨ āĻĒāĻžāĻ“āĻ˛āĻž āĻ¤āĻžāĻšāĻžāĻ° āĻ¸ā§‡āĻ‡ āĻŦā§‡āĻĄāĻŧāĻžāĻ° āĻ‰āĻĒāĻ¨āĻŋāĻˇā§āĻŸāĻŋ āĻāĻ•āĻ–āĻ¨ āĻĻā§‡āĻ–āĻŋāĻŦāĻžāĻ° āĻļāĻ•ā§āĻ¤āĻŋāĻĒāĻ•ā§āĻ¤ āĻ•āĻ°āĻžāĻ‡āĻ˛ā§‡āĻ¨āĨ¤
āĻ†āĻŽāĻžāĻ•ā§‡ āĻŦāĻŋāĻ¨ā§āĻĻā§āĻ•ā§‡ āĻ¨āĻŋāĻĻā§āĻ°āĻŽ āĻšāĻ“āĻ¯āĻŧāĻžāĻ° āĻļāĻžāĻāĻ•ā§‡ āĻĢā§‡āĻ˛āĻŋāĻ•āĨ¤ āĻ•āĻžāĻŽāĻĄāĻŧāĻžāĻšā§āĻ›-āĻ–ā§‡āĻ¯āĻŧā§‡ āĻ¯āĻœā§āĻžā§‡āĻļā§āĻŦāĻ°ā§‚āĻĒā§‡ āĻ§ā§€āĻ°ā§‡ āĻ†āĻŽāĻžāĻ° āĻ ā§‡āĻ˛āĻŋāĻ¯āĻŧāĻž āĻ­āĻžāĻ˛ā§‹ āĻ¨āĻžāĻ‡ āĻ¤āĻž āĻ†āĻŦāĻžāĻ° āĻŦāĻ˛āĻ˛ āĻ¯āĻ–āĻ¨ āĻ¨āĻž'āĻ•āĻžāĻĄāĻŧā§‡āĻ° āĻ‰āĻĒāĻ° āĻŦāĻŋāĻļā§‡āĻˇ āĻ‰āĻĒāĻ° āĻ āĻŋāĻ•ā§‡ āĻ¯āĻžāĻ‡āĻ¤, āĻ•ā§‡āĻŦāĻ˛ āĻŽāĻ¨ā§‡ āĻ•āĻ°āĻž āĻĒāĻĄāĻŧ āĻŦā§āĻ°āĻ¤āĻŋāĻĻāĻŋāĻ¨ā§‡ āĻ†āĻ°ā§‹ āĻ˛āĻĄāĻŧ āĻĻāĻŋāĻ¯āĻŧāĻž āĻ†āĻļāĻ¯āĻŧ āĻĻāĻŋāĻ¯āĻŧāĻž āĻ¸ā§‡ 		āĻŦā§āĻāĻŋāĻ¤ā§‡ āĻšāĻ¯āĻŧ āĻ¨āĻžāĨ¤
āĻ‡āĻ‚āĻ°ā§‡āĻœāĻŋ āĻĒāĻĄāĻŧāĻž āĻœā§€āĻŦāĻ¨ āĻ—āĻžāĻ¯āĻŧā§‡ āĻšāĻ˛āĻŋāĻ¯āĻŧāĻž āĻ—ā§‡āĻ˛āĨ¤
āĻ¤āĻ–āĻ¨ āĻāĻ•āĻŸāĻŋ āĻ†āĻ¯āĻŧā§‹āĻœāĻ¨āĻĻāĻžāĻ°ā§āĻ¯ āĻŽāĻžāĻĻāĻ•ā§‡ āĻŦāĻ˛āĻŋāĻŦā§‡ āĻ¨āĻž, āĻ¨āĻ¤ā§āĻĒā§‚āĻŽāĻ˛ā§€ āĻ¨āĻž āĻĻā§‡āĻ–āĻž āĻāĻ•āĻŸāĻŋ āĻŽā§‡āĻ¯āĻŧā§‡āĻŸāĻŋ

Iteration: 511000

āĻ¨āĻž, āĻ¤āĻŦā§ āĻ¯ā§‡āĻŽāĻ¨ āĻ˛āĻžāĻŦāĻŖā§āĻ¯ āĻĒā§āĻ°āĻžāĻŽāĻ˛āĻž āĻ›āĻžāĻĄāĻŧāĻŋāĻ¯āĻŧāĻž āĻĻāĻŋāĻ˛, āĻ¤āĻžāĻšāĻžāĻĻā§‡āĻ° āĻāĻŽāĻ¨ āĻ¸āĻžāĻĻāĻžāĻ¸āĻŋāĻ§āĻž āĻŦāĻ˛āĻŋāĻ˛, 'āĻ¤ā§āĻŽāĻŋ āĻ¤ā§‹āĻ˛āĻž āĻšāĻžāĻ¸āĻŋ āĻ†āĻ° āĻ•ā§‡āĻ‰ āĻ›ā§‡āĻ˛ā§‡āĻŽāĻžāĻ¨ā§āĻˇ āĻ¨āĻžāĻ‡āĨ¤'
āĻ¸āĻ¤ā§€āĻļāĨ¤ āĻĻā§āĻŸāĻŋ āĻ­ā§€āĻ¤ āĻŽā§‡āĻœāĻŋāĻ¯āĻŧāĻž āĻŦāĻ˛āĻŋāĻ˛āĻžāĻŽ, 'āĻĻāĻžāĻĻāĻž, āĻ¤ā§‹āĻŽāĻžāĻ•ā§‡ āĻ—ā§‡āĻ˛āĻžāĻŽ āĻ¨āĻžāĨ¤ āĻĒāĻĨāĻŋāĻ•ā§‡āĻ°āĻž āĻ–āĻžāĻ¤āĻžāĨ¤ āĻšāĻŦāĻŋāĻ° āĻŸā§‡āĻ¨ā§‡ āĻ‰āĻĒāĻŦāĻžāĻ¸ āĻ˛āĻžāĻ—āĻ˛ā§‡āĨ¤ āĻ…āĻ¨ā§āĻ¯āĻžāĻ¯āĻŧ āĻ¸āĻ•āĻ˛ā§‡āĻ° āĻ¸āĻžāĻ°ā§āĻœāĻ¨ā§‡ āĻ†āĻŽāĻžāĻĻā§‡āĻ° āĻŦāĻžāĻĄāĻŧāĻŋāĻ° āĻ¸ā§‚āĻ•ā§āĻˇā§āĻŖ āĻĒā§āĻŸā§‡āĻ° āĻ­āĻžāĻŸāĻžāĻŸāĻž āĻŦā§‹āĻ¨ā§‡āĻ° āĻĒāĻĻāĻ¨āĻžāĻ° āĻ‰āĻĒāĻ° āĻŦāĻšāĻŋāĻ¯āĻŧāĻž āĻ…āĻ¸ā§āĻĨāĻŋāĻ° āĻ•āĻ°āĻŋāĻ¯āĻŧāĻž āĻĒāĻžāĻ‡āĨ¤ 		 	 āĻā§‹āĻ•āĻ¸ā§āĻŦāĻ˛ā§€āĻ¨āĻžāĻĒāĻžāĻ¨āĻ•ā§‡ āĻšāĻŋāĻšā§āĻ¨ āĻ˛āĻ‡āĻ¯āĻŧāĻž āĻĻāĻžāĻŸāĻŋāĻ° āĻ¨āĻ¨ā§€āĻ° āĻŽāĻ§ā§āĻ¯ āĻšāĻ‡āĻ¤ā§‡ āĻĒāĻ°āĻŦāĻžāĻ° āĻ¸āĻšāĻ¯āĻžāĻ¤ā§āĻ°ā§€ āĻŦāĻ˛āĻŋāĻŦāĨ¤ āĻ¨āĻŋāĻœā§‡āĻ•ā§‡ āĻŸā§‡āĻāĻ•ā§‡ āĻ¨āĻžāĨ¤ āĻ†āĻœ āĻ¤ā§‹āĻŽāĻžāĻ•ā§‡ āĻ†āĻŽāĻžāĻ° āĻŦāĻžāĻĄāĻŧāĻŋāĻ° āĻ‡āĻšā§āĻ›āĻž āĻšāĻ¯āĻŧā§‡ āĻ‰āĻ ā§‡āĨ¤
āĻ‰āĻ˛ā§āĻŸāĻž āĻ•āĻ°āĻŋāĻŦā§‡āĻ¨, 'āĻšā§‡āĻŽāĻĨāĻžāĻ°āĻž āĻ˛āĻ•ā§āĻˇā§āĻ¯ āĻ•āĻ°ā§‡ āĻ—ā§‡āĻ˛āĨ¤ āĻ‡āĻ¤āĻŋāĻŽāĻ§ā§āĻ¯ā§‡ āĻ¸āĻŽāĻ¸ā§āĻ¤ āĻ¯āĻ¤ā§āĻ¨ā§‡ āĻŦāĻžāĻšāĻŋāĻ° āĻšāĻ‡āĻ¤ā§‡ āĻĒāĻ°āĻŋāĻ¤ā§‡ āĻšāĻžāĻœāĻžāĻ° āĻĻā§€āĻĒ

Author

Greg (Grzegorz) Surma

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