Tips for Adaptation and Extending

General

Many customisations can be achieved by sub-classing objects from the elektronn.training package or adding new functions in the layers, e.g.:

• If you want to create another network architecture or use layer options which are not configurable through the config file (e.g. input noise or weight sharing between layers) you can still use the pipeline but make a sub-class of elektronn.training.trainer.Trainer in which the createNet method is overridden by your own code and everything else is inherited. Then you must only make sure that in the script elektronn-train your own Trainer class is used by exchanging the class that is imported.
• If you want different weight initialisation, you can override the randomizeWeights methods in the layer classes (e.g. there is already fragmentary code for creating Gabor filters for 2D conv layers)
• If you create your own data class (e.g. as a subclass of the MNIST pipeline of elektronn.training.traindata) you can use it via the config option data_class_name

Caveats

• In the training pipeline, it is not possible to have all imports at the top of the file because some imports can only be made after some conditions are fulfilled (e.g. previous to any theano imports the device must be initialised - otherwise the value from .theanorc is used and it cannot be changed later on)
• When adding new configuration options to the pipeline, you must also put them into the master config in training/config.py otherwise they will be considered as invalid/non-existing parameters.
• For 3D data the backend has axis order (z,x,y) for performance reasons. But on the frontend filter and input shapes are defined as (x,y,z) and subsequently swapped internally. The full internal processing order for 3D convolutions is (batch, z, channel, x, y). Bear this in mind when looking at internal code involving 3D data.