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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 >= 0.7.0
  • NumPy >= 1.10.0

Follow the instructions on TensorFlow official website to install TensorFlow.

If you use their pip installation:

# Ubuntu/Linux 64-bit, CPU only:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled. Requires CUDA toolkit 7.5 and CuDNN v4.  For
# other versions, see "Install from sources" below.
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

# Mac OS X, CPU only:
$ sudo easy_install --upgrade six
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.8.0-py2-none-any.whl

It will also install other necessary packages (including NumPy) for you.

Test

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

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):

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

All the output of this experiment will be saved in a folder (default to output/, you can specify the folder name using --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 (then it will be saved in 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 assume the data file is using utf-8 encoding by default, use --encoding=your-encoding to specify the encoding if your data file cannot be decoded using utf-8.

Sampling

To sample from the best model of an experiment (with a given start_text and length):

python sample.py --init_dir=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] (https://www.tensorflow.org/versions/r0.8/how_tos/summaries_and_tensorboard/index.html#tensorboard-visualizing-learning) (the "events" tab) and the computation graph (the "graph" tab).

First run:

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:

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

Hyperparameter tuning

train.py provides a list of hyperparameters you can tune.

To see the list of all hyperparameters, run:

python train.py --help