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Latest commit a803bf4 Jun 15, 2016 @orionr orionr Add to neural_gpu documentation.
* Switch from decimal to symbol in most places and add a description of what a symbol is per Lukasz.

README.md

NeuralGPU

Code for the Neural GPU model as described in [[http://arxiv.org/abs/1511.08228]].

Requirements:

  • TensorFlow (see tensorflow.org for how to install)
  • Matplotlib for Python (sudo apt-get install python-matplotlib)

The model can be trained on the following algorithmic tasks:

  • sort - Sort a symbol list
  • kvsort - Sort symbol keys in dictionary
  • id - Return the same symbol list
  • rev - Reverse a symbol list
  • rev2 - Reverse a symbol dictionary by key
  • incr - Add one to a symbol value
  • add - Long decimal addition
  • left - First symbol in list
  • right - Last symbol in list
  • left-shift - Left shift a symbol list
  • right-shift - Right shift a symbol list
  • bmul - Long binary multiplication
  • mul - Long decimal multiplication
  • dup - Duplicate a symbol list with padding
  • badd - Long binary addition
  • qadd - Long quaternary addition
  • search - Search for symbol key in dictionary

The value range for symbols are defined by the niclass and noclass flags. In particular, the values are in the range min(--niclass, noclass) - 1. So if you set --niclass=33 and --noclass=33 (the default) then --task=rev will be reversing lists of 32 symbols, and --task=id will be identity on a list of up to 32 symbols.

To train the model on the reverse task run:

python neural_gpu_trainer.py --task=rev

While training, interim / checkpoint model parameters will be written to /tmp/neural_gpu/.

Once the amount of error gets down to what you're comfortable with, hit Ctrl-C to stop the training process. The latest model parameters will be in /tmp/neural_gpu/neural_gpu.ckpt-<step> and used on any subsequent run.

To test a trained model on how well it decodes run:

python neural_gpu_trainer.py --task=rev --mode=1

To produce an animation of the result run:

python neural_gpu_trainer.py --task=rev --mode=1 --animate=True

Maintained by Lukasz Kaiser (lukaszkaiser)