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deep-morphology's Issues

Refactoring

Dataset

  • __len__: first dimension of the first matrix
  • use namedtuples everywhere

Models

  • the encoder is the same in every seq2seq model, move it to a separate file and import it everywhere
  • the decoder is pretty similar in a few models, base class?
  • should attention mechanism be in a separate class?
    • pro: can use different attention variations easily

train list of configs

Similar to train_many but no randomness.

List of configs

Python or YAML?

Python. It allows creating configurations in a flexible way.

Implementation

Take a base configuration and override certain parameters. Should be implemented as a generator function. A single Python file which defines this generator should be the parameter of the runner script..

Consistency in nn module parameters

Some of the neural modules take a config object, others take parameters separately. This should be more consistent, for example

  1. top-level modules should take a single config object and a dataset object. Top-level modules are the ones that can be specified in config.yaml
  2. other modules should take each parameter separately. The reason for this is that they are designed to be reusable blocks for other modules.

files missing?

Hi

Can't run toy example as there are no toy.yaml in root/config and root/data dir.
Are these in an earlier PR / diff repo? Ta

Attention variants

Currently Luong's attention with a projection matrix is the only attention variant supported. Should I add more variants?

Tests

Run toy training for every model.

debug switch

Add debug switch. Only print a warning if there are modifications in the working directory.

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