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)