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Memory Augmented Neural Networks (MANN) Repository for D-NTM

The codes to implement different types of Memory Augmented Neural Networks in [1] and more...

In this repo, you can find codes to reproduce the results on,

  1. Facebook's bAbI dataset.
  2. Permuted MNIST
  3. NTM Toy Tasks

Information about the codes and the files structure

  1. codes/, the general codes of the model of memory models.

    1.1. codes/core includes the core generic layers, mainloops, learning rules and some useful utilities to implement neural memory models safely and efficiently with Theano. The most important files are:

     1.1.1. `operators.py`: includes the different operators for example a class to implement the similaritties between the key and the content in the memory. Or a class to implement the REINFORCE as `known_grads` of the `tensor.grad` function in Theano.
         * For the implementation of REINFORCE with moving averages baseline see, `REINFORCE` class.
         * For the input based baseline see, `REINFORCEBaselineExt` class.
     1.1.2. `costs.py`: this file includes different cost functions for our memory model implemented efficiently in theano. For example, `huber_loss` used for the input based baseline is implemented in this file.
     1.1.3. `penalty.py` different penalties and regularizers are implemented in this file. For example, we have an alternative implementation of REINFORCE (both input based or regular baseline), implemented in this folder.

    1.2. codes/memnet includes the layers, mainloops and data iterators specific to implement ntm and dntm type of memory models. Some important files are,

     1.2.1. `addresser.py` includes different type of implementations of addressing types.
     1.2.2. `memory.py` implements the external memory mechanism for NTM/D-NTM models.
     1.2.3. `nmodel.py` combines different layers and implements the codes and core computation graph for the MANNs (using this and by changing the configs of thise file it is possible to implement various types of external memory models.)
     1.2.4. `ntm_layers.py` implements the controllers and different heads (disables and enables, some of them are based on the options that are provided as the arguments to the class or the function).
     1.2.5. `controllers.py` basic controllers for the MANN (supports either FF or Recurrent-GRU/LSTM).
     1.2.6. `mainloop.py` implements different types of mainloop for different types of NTMs and the controllers.
     1.2.7. `fbBIdataiterator.py` implements the data iterator for the bAbI dataset.
     1.2.8. `babi_data_processing/` folder contains the scripts to preprocess the bAbI dataset.
  2. scripts, the scripts to run the models and the experiments with different types of MANN models.

  3. experiments, some of the scripts that we used for the hyperparameter search for our models.

[1] Gulcehre C, Chandar S, Cho K, Bengio Y. Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes. arXiv preprint arXiv:1607.00036. 2016 Jun 30.

Open Source Agenda is not affiliated with "Dntm" Project. README Source: caglar/dntm
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