was already solved, just updated documentation

mlair/model_modules/model_class.py

@@ -2,7 +2,7 @@
 Module for neural models to use during experiment.
 
 To work properly, each customised model needs to inherit from AbstractModelClass and needs an implementation of the
-set_model and set_loss method.
+set_model method.
 
 In this module, you can find some exemplary model classes that have been build and were running in a experiment.
 
@@ -33,10 +33,11 @@ How to create a customised model?
 
                 # apply to model
                 self.set_model()
-                self.set_loss()
-                self.set_custom_objects(loss=self.loss)
+                self.set_compile_options()
+                self.set_custom_objects(loss=self.compile_options['loss'])
 
-* Make sure to add the `super().__init__()` and at least `set_model()` and  `set_loss()` to your custom init method.
+* Make sure to add the `super().__init__()` and at least `set_model()` and `set_compile_options()` to your custom init
+  method.
 * If you have custom objects in your model, that are not part of keras, you need to add them to custom objects. To do
   this, call `set_custom_objects` with arbitrarily kwargs. In the shown example, the loss has been added, because it
   wasn't a standard loss. Apart from this, we always encourage you to add the loss as custom object, to prevent
@@ -60,14 +61,20 @@ How to create a customised model?
                 self.model = keras.Model(inputs=x_input, outputs=[out_main])
 
 * Your are free, how to design your model. Just make sure to save it in the class attribute model.
-* Finally, set your custom loss.
+* Additionally, set your custom compile options including the loss.
 
     .. code-block:: python
 
         class MyCustomisedModel(AbstractModelClass):
 
-            def set_loss(self):
+            def set_compile_options(self):
+                self.initial_lr = 1e-2
+                self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
+                self.lr_decay = mlair.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr,
+                                                                                       drop=.94,
+                                                                                       epochs_drop=10)
                 self.loss = keras.losses.mean_squared_error
+                self.compile_options = {"metrics": ["mse", "mae"]}
 
 * If you have a branched model with multiple outputs, you need either set only a single loss for all branch outputs or
   to provide the same number of loss functions considering the right order. E.g.
@@ -80,7 +87,7 @@ How to create a customised model?
                 ...
                 self.model = keras.Model(inputs=x_input, outputs=[out_minor_1, out_minor_2, out_main])
 
-            def set_loss(self):
+            def set_compile_options(self):
                 self.loss = [keras.losses.mean_absolute_error] +  # for out_minor_1
                             [keras.losses.mean_squared_error] +   # for out_minor_2
                             [keras.losses.mean_squared_error]     # for out_main
@@ -111,7 +118,6 @@ True
 import mlair.model_modules.keras_extensions
 
 __author__ = "Lukas Leufen, Felix Kleinert"
-# __date__ = '2019-12-12'
 __date__ = '2020-05-12'
 
 from abc import ABC