TensorFlow-Char-RNN

A TensorFlow implementation of Andrej Karpathy's Char-RNN, a character level language model using multilayer Recurrent Neural Network (RNN, LSTM or GRU). See his article The Unreasonable Effectiveness of Recurrent Neural Network to learn more about this model.

Installation

Dependencies

• Python 2.7
• TensorFlow >= 1.2

Follow the instructions on TensorFlow official website to install TensorFlow.

Test

If the installation finishes with no error, quickly test your installation by running:

bash
python train.py --data_file=data/tiny_shakespeare.txt --num_epochs=10 --test

This will train char-rnn on the first 1000 characters of the tiny shakespeare copus. The final train/valid/test perplexity should all be lower than 30.

Usage

• train.py

  is the script for training.

• sample.py

  is the script for sampling.

• char_rnn_model.py

  implements the Char-RNN model.

Training

To train on tiny shakespeare corpus (included in data/) with default settings (this might take a while):

bash
python train.py --data_file=data/tiny_shakespeare.txt

All the output of this experiment will be saved in a folder (default to

output/

--output_dir=your-output-folder

The experiment log will be printed to stdout by default. To direct the log to a file instead, use

--log_to_file

your-output-folder/experiment_log.txt

The output folder layout:

  your-output-folder
    ├── result.json             # results (best validation and test perplexity) and experiment parameters.
    ├── vocab.json              # vocabulary extracted from the data.
    ├── experiment_log.txt      # Your experiment log if you used --log_to_file in training.
    ├── tensorboard_log         # Folder containing Logs for Tensorboard visualization.
    ├── best_model              # Folder containing saved best model (based on validation set perplexity)
    ├── saved_model             # Folder containing saved latest models (for continuing training).

Note:

train.py

--encoding=your-encoding

Sampling

To sample from the best model of an experiment (with a given starttext and length): ```bash python sample.py --initdir=your-output-folder --start_text="The meaning of life is" --length=100 ```

Visualization

To use Tensorboard (a visualization tool in TensorFlow) to visualize the learning (the "events" tab) and the computation graph (the "graph" tab).

First run:

bash
tensorboard --logdir=your-output-folder/tensorboard_log

Then navigate your browser to http://localhost:6006 to view. You can also specify the port using

--port=your-port-number

Continuing an experiment

To continue a finished or interrupted experiment, run:

bash
python train.py --data_file=your-data-file --init_dir=your-output-folder

Hyperparameter tuning

train.py

To see the list of all hyperparameters, run:

bash
python train.py --help