|Number of watchers on Github||1850|
|Number of open issues||45|
|Average time to close an issue||17 days|
|Average time to merge a PR||13 days|
|Open pull requests||29+|
|Closed pull requests||10+|
|Last commit||over 2 years ago|
|Repo Created||about 4 years ago|
|Repo Last Updated||almost 2 years ago|
|Organization / Author||sherjilozair|
|Do you use char-rnn-tensorflow? Leave a review!|
|View open issues (45)|
|View char-rnn-tensorflow activity|
|View on github|
|Fresh, new opensource launches 🚀🚀🚀|
Trendy new open source projects in your inbox! View examples
Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow.
Inspired from Andrej Karpathy's char-rnn.
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,
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
To visualize training progress, model graphs, and internal state histograms: fire up Tensorboard and point it at your
$ tensorboard --logdir=./logs/
Then open a browser to http://localhost:6006 or the correct IP/Port specified.
Please feel free to: