Differentiable Neural Computer, for Pytorch

This is an implementation of Differentiable Neural Computers, described in the paper Hybrid computing using a neural network with dynamic external memory, Graves et al.

Install

pip install dnc

Usage

Parameters:

Argument	Default	Description
input_size	None	Size of the input vectors
hidden_size	None	Size of hidden units
rnn_type	'lstm'	Type of recurrent cells used in the controller
num_layers	1	Number of layers of recurrent units in the controller
bias	True	Bias
batch_first	True	Whether data is fed batch first
dropout	0	Dropout between layers in the controller (Not yet implemented)
bidirectional	False	If the controller is bidirectional (Not yet implemented)
nr_cells	5	Number of memory cells
read_heads	2	Number of read heads
cell_size	10	Size of each memory cell
nonlinearity	'tanh'	If using 'rnn' as rnn_type, non-linearity of the RNNs
gpu_id	-1	ID of the GPU, -1 for CPU
independent_linears	False	Whether to use independent linear units to derive interface vector
share_memory	True	Whether to share memory between controller layers
reset_experience	False	Whether to reset memory (This is a parameter for the forward pass)

Example usage:

from dnc import DNC

rnn = DNC(
  input_size=64,
  hidden_size=128,
  rnn_type='lstm',
  num_layers=4,
  nr_cells=100,
  cell_size=32,
  read_heads=4,
  batch_first=True,
  gpu_id=0
)

(controller_hidden, memory, read_vectors) = (None, None, None)

output, (controller_hidden, memory, read_vectors) = \
  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))

Example copy task

The copy task, as descibed in the original paper, is included in the repo.

python ./copy_task.py -cuda 0

General noteworthy stuff

1. DNCs converge with Adam and RMSProp learning rules, SGD generally causes them to diverge.
2. Using a large batch size (> 100, recommended 1000) prevents gradients from becoming NaN.