| # Train Char-RNN over plain text |
| |
| Recurrent neural networks (RNN) are widely used for modelling sequential data, |
| e.g., natural language sentences. This example describes how to implement a RNN |
| application (or model) using SINGA's RNN layers. |
| We will use the [char-rnn](https://github.com/karpathy/char-rnn) model as an |
| example, which trains over sentences or |
| source code, with each character as an input unit. Particularly, we will train |
| a RNN using GRU over Linux kernel source code. After training, we expect to |
| generate meaningful code from the model. |
| |
| |
| ## Instructions |
| |
| * Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05. |
| |
| * Prepare the dataset. Download the [kernel source code](http://cs.stanford.edu/people/karpathy/char-rnn/). |
| Other plain text files can also be used. |
| |
| * Start the training, |
| |
| python train.py linux_input.txt |
| |
| Some hyper-parameters could be set through command line, |
| |
| python train.py -h |
| |
| * Sample characters from the model by providing the number of characters to sample and the seed string. |
| |
| python sample.py 'model.bin' 100 --seed '#include <std' |
| |
| Please replace 'model.bin' with the path to one of the checkpoint paths. |
| |
