This is a fork of https://github.com/sherjilozair/char-rnn-tensorflow with modifications to enable the trained models to be used in other environments (e.g. ofxMSATensorFlow). Reasons as to why these changes are nessecary are described here.
After training, run:
sample.py with the --freeze_graph argument to prune, freeze and save the graph as a binary protobuf to be loaded in C++ (removing unnessecary nodes used in training, and replacing variables with consts). It also saves the character-index map as a text file.
sample_frozen.py demonstrates inference with the frozen graph from python. It also works in C++/openFrameworks.
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 sample from a checkpointed model, python sample.py.
- Add explanatory comments
- Expose more command-line arguments
- Compare accuracy and performance with char-rnn