A PyTorch implementation of char-rnn for character-level text generation. This is copied from the Practical PyTorch series.
Download this Shakespeare dataset (from the original char-rnn) as shakespeare.txt. Or bring your own dataset — it should be a plain text file (preferably ASCII).
Run train.py with the dataset filename to train and save the network:
> python train.py shakespeare.txt
Training for 2000 epochs...
(... 10 minutes later ...)
Saved as shakespeare.pt
After training the model will be saved as [filename].pt.
Usage: train.py [filename] [options]
Options:
--model Whether to use LSTM or GRU units gru
--n_epochs Number of epochs to train 2000
--print_every Log learning rate at this interval 100
--hidden_size Hidden size of GRU 50
--n_layers Number of GRU layers 2
--learning_rate Learning rate 0.01
--chunk_len Length of training chunks 200
--batch_size Number of examples per batch 100
--cuda Use CUDA
Run generate.py with the saved model from training, and a "priming string" to start the text with.
> python generate.py shakespeare.pt --prime_str "Where"
Where, you, and if to our with his drid's
Weasteria nobrand this by then.
AUTENES:
It his zersit at he
Usage: generate.py [filename] [options]
Options:
-p, --prime_str String to prime generation with
-l, --predict_len Length of prediction
-t, --temperature Temperature (higher is more chaotic)
--cuda Use CUDA
