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One-shot Learning with Memory-Augmented Neural Networks

Tensorflow implementation of the paper One-shot Learning with Memory-Augmented Neural Networks, by A. Santoro et al. This is an offshoot of a larger project, which aims to synthesize bio-plausible neural networks that solve cognitive tasks.

This implementation is much simpler than a lot of others out there, thanks to TensorFlow's API and ease of use. The model as described in the paper has been followed as closely as possible.

The code is inspired by tristandeleu's excellent Theano implementation.

Requirements

This implementation requires

• numpy >= 1.12.1
• scipy >= 0.17.0
• tensorflow >= 1.0
• matplotlib >= 1.5.1

Dataset

All datasets should be placed in the data folder. In order to run the Omniglot experiment, download the dataset from here and unzip [python/images_background.zip] in the data/omniglot folder.

Running

python omniglot.py

Run python omniglot.py --help to see a list of available options

--num-classes and --num-samples specify how to form an episode of input data from the omniglot dataset.

--controller-size specifies the number of hidden units in the LSTM controller network.

--memory-locations specifies the number of memory locations contained in the external memory.

--memory-word-size specifies the size of each word stored in external memory.

--learning-rate and --iterations specify the parameters for training the model.

The default values for all parameters are those used in the paper.

Paper

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap, One-shot Learning with Memory-Augmented Neural Networks, [arXiv]