Move training functions into model classes

[florian-huber]

This refers to the pytorch version currently being developed.
We should probably move the training functions into a train() method of each respective model class, for clarity.

[niekdejonge]

@florian-huber I am implementing this now.
We have a few settings that are now stored in SettingsMS2Deepscore (e.g. settings.evaluator_num_filters), but others have to be provided separately and are therefore not stored when saving the model (like mini_batch_size). I understand the separation, the first ones are necessary to run the model, while the others are only relevant during training. But I would prefer to store all training settings when saving a model, to make it easy for us (and other developers) to check which settings were used during training.
Shall I add these settings to init as well, to ensure that these are saved as well?

[niekdejonge]

And if we want to add them, shall I add them all to SettingsMS2Deepscore or shall I make a new Class EmbeddingsEvaluationModelSettings, or just add them as separate parameters in init of EmbeddingsEvaluator