korymath / char-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

char-rnn-tensorflow

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

Inspired from Andrej Karpathy's char-rnn.

Requirements

• Tensorflow 1.0

Basic Usage

To train with default parameters on the tinyshakespeare corpus, run python train.py. To access all the parameters use python train.py --help.

To sample from a checkpointed model, python sample.py.

Datasets

You can use any plain text file as input. For example you could download The complete Sherlock Holmes as such:

cd data
mkdir sherlock
cd sherlock
wget https://sherlock-holm.es/stories/plain-text/cnus.txt
mv cnus.txt input.txt

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

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

Tensorboard

To visualize training progress, model graphs, and internal state histograms: fire up Tensorboard and point it at your log_dir. E.g.:

$ tensorboard --logdir=./logs/

Then open a browser to http://localhost:6006 or the correct IP/Port specified.

Roadmap

• Add explanatory comments
• Expose more command-line arguments
• Compare accuracy and performance with char-rnn
• More Tensorboard instrumentation

Contributing

Please feel free to:

• Leave feedback in the issues
• Open a Pull Request
• Join the gittr chat
• Share your success stories and data sets!