improve callback handling

This issue emerged during #30 (closed). The enhanced model checkpoint takes all callbacks to save as argument, but meanwhile this callbacks need also to be parsed to the fit_generator function in addition to the checkpoint. See the following example:

1. define learning rate scheduler lr = LearningRateDecay(base_lr=1e-2, drop=.94, epochs_drop=10)
2. add this scheduler to the enhanced checkpoint ckpt = ModelCheckpointEnhanced(...., callbacks_to_save=lr, ...)
3. but also make sure, that it is added to the model history = model.fit_generator(..., callbacks=[ckpt, lr], ...) This is not intuitive, because the same object needs to be added more than once. Any change on the callbacks requires two changes in the code.

-> Think about a solution to add all callbacks in one place and create a class (or what ever) that can handle this.

SOLUTION:

• New class CallbackHandler
• add all callbacks to this class via .add_callback(<obj>, <path>, <name(opt)>)
• create advanced model checkpoint inside this class via .create_model_checkpoint(**kwargs)
• add callbacks e.g. to fit_generator() using kwarg callbacks=CallbackHandler.get_callbacks(as_dict=False)
• the command above takes care, to add all callbacks in a consistent order and the checkpoint as last element (this is required, because it triggers the local save of all previous mentioned callbacks)
• to update the callbacks by previous saved data, just call .load_callbacks() and update the checkpoint afterwards with .update_checkpoint(history_name=<default:"hist">)
• specific callbacks can always be accessed with .get_callback_by_name(<name>)