Models

Models can, but may not be your way to set up your machine learning model. For simple feed-forward networks it is a good idea to implement them as a model class (inherited from object) with simple input and output methods. Usually the whole actual model in between is defined in the __init__ method.

The iterator then takes the model as one of its arguments and adds the optimizer logic to the respective model. This allows for easy exchange between models that only requires changing one line of code in the config.yaml.

More advanced models that may require to reuse parts of the model should only define the architecture but leave the inputs and outputs to the iterator.