word-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

Mostly reused code from https://github.com/sherjilozair/char-rnn-tensorflow which was inspired from Andrej Karpathy's char-rnn.

Requirements

• Tensorflow 1.1.0rc0

Basic Usage

To train with default parameters on the tinyshakespeare corpus, run:

python train.py

To sample from a trained model

python sample.py

To pick using beam search, use the --pick parameter. Beam search can be further customized using the --width parameter, which sets the number of beams to search with. For example:

python sample.py --pick 2 --width 4

To change the number of words to sample, use the -n parameter. Default is set to 200 words. For example:

python sample.py -n 500

To change the number of samples to print, use the -c or --count parameter. For example:

python sample.py -c 4

Training / Sampling on a custom dataset

NEW & IMPORTANT INFO:
You can use any plain text file as input. To train on a custom dataset, create an input.txt file in a new folder within data, and create an empty folder with the same name within save.

Then start train from the top level directory using python train.py --data_dir=./data/YOUR_FOLDER_HERE/ --save_dir=./save/YOUR_FOLDER_HERE

A quick tip to concatenate many small disparate .txt files into one large training file: ls *.txt | xargs -L 1 cat >> input.txt

After training on the custom data, start sample from the top level directory using python sample.py --save_dir=./save/YOUR_FOLDER_HERE. To edit the default --data_dir and --save_dir paths, see lines 15 & 21 of train.py. Be sure to pay attention to the respective default paths in lines 15 & 48 of sample.py as well. If successful, you will no longer need to include the data and save directories in the command prompt.

Sample output

Word-RNN

LEONTES:
Why, my Irish time?
And argue in the lord; the man mad, must be deserved a spirit as drown the warlike Pray him, how seven in.

KING would be made that, methoughts I may married a Lord dishonour
Than thou that be mine kites and sinew for his honour
In reason prettily the sudden night upon all shalt bid him thus again. times than one from mine unaccustom'd sir.

LARTIUS:
O,'tis aediles, fight!
Farewell, it himself have saw.

SLY:
Now gods have their VINCENTIO:
Whipt fearing but first I know you you, hinder truths.

ANGELO:
This are entitle up my dearest state but deliver'd.

DUKE look dissolved: seemeth brands
That He being and
full of toad, they knew me to joy.

Char-RNN

ESCALUS:
What is our honours, such a Richard story
Which you mark with bloody been Thilld we'll adverses:
That thou, Aurtructs a greques' great
Jmander may to save it not shif theseen my news
Clisters it take us?
Say the dulterout apy showd. They hance!

AnBESS OF GUCESTER:
Now, glarding far it prick me with this queen.
And if thou met were with revil, sir?

KATHW:
I must not my naturation disery,
And six nor's mighty wind, I fairs, if?

Messenger:
My lank, nobles arms;

Beam search

Beam search differs from the other --pick options in that it does not greedily pick single words; rather, it expands the most promising nodes and keeps a running score for each beam.

Word-RNN (with beam search)

# python sample.py --prime "KING RICHARD III:" -n 100 --pick 2 --width 4

KING RICHARD III:
you, and and and and have been to be hanged, I am not to be touched?

Provost:
A Bohemian born, for tying his own train,
Forthwith by all that converses more with a crow-keeper;
I have drunk, Broach'd with the acorn cradled. Follow.

FERDINAND:
Who would not be conducted.

BISHOP OF ELY:
If you have been a-bed an acre of barren ground, hath holy;
I warrant, my lord restored of noon.

ISABELLA:
'Save my master and his shortness whisper me to the pedlar;
Money's a medler.
That I will pamper it to complain.

VOLUMNIA:
Indeed, I am

Word-RNN (without beam search)

# python sample.py --prime "KING RICHARD III:" -n 100

KING RICHARD III:
marry, so and unto the wind have yours;
And thou Juliet, sir?

JULIET:
Well, wherefore speak your disposition cousin;
May thee flatter.
My hand will answer him;
e not to your Mariana Below these those and take this life,
That stir not light of reason.
The time Lucentio keeps a root from you.
Cursed be his potency,
It was my neighbour till the birth and I drank stay.

MENENIUS:
Here's the matter,
I know take this sour place,
they know allegiance Had made you guilty.
You do her bear comfort him between him or our noble bosom he did Bolingbroke's

NOTES:

• Avoid using beam search (or --pick 2) in conjunction with --prime when generating multiple samples with --count (or -c). This will likely give you multiple identical samples or repeating text.

• If you notice the outputted text is starting to repeat itself anyway, it may be helpful to remove said repeated phrases from the training data and re-train.

• Alternatively, you may also try increasing the --count parameter while keeping the -n (number of words to sample) parameter relatively low. This will generate a larger number of smaller samples.