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Feature/472 create model abstraction for declearn integration

MR description

Implements model abstraction for pytorch and scikit learn Closes #472 (closed).

Here a list of points that i found difficult to implement (and that need a closer look).

  1. BaseSklearnModel:
  • batch_size computation / reset that is done internally
  • sklearn model n_iter attribute increment/decrement (hidden through methods)
  • apply_updates method: slightly modified from the one of the poc, that adds gradients instead of changing them, in the same spirit as the apply_updates of pytorch. Check if computation is correct
  • Toolbox classes, that implement some method using multiple inheritance
  1. TorchModel
  • method train not implemented: should we compute loss in this method ?
  • is get_gradients method correct?
  • saving state of the TorchModel: initial parameters are saved in a specific attribute:
  • make sure it handles frozen layers

More broadly speaking:

  • appropriate raise of exceptions: some exceptions may not be caught
  • appropriate naming of variables
  • create multiple modules for each classes (ie one class per file) rather than having 3 classes in a file.

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Edited by BOUILLARD Yannick

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