include bug fix, /close #154

mlair/model_modules/model_class.py

@@ -23,13 +23,13 @@ How to create a customised model?
 
         class MyCustomisedModel(AbstractModelClass):
 
-            def __init__(self, window_history_size, window_lead_time, channels):
-                super.__init__()
+            def __init__(self, shape_inputs: list, shape_outputs: list):
+
+                super().__init__(shape_inputs[0], shape_outputs[0])
+
                 # settings
-                self.window_history_size = window_history_size
-                self.window_lead_time = window_lead_time
-                self.channels = channels
                 self.dropout_rate = 0.1
+                self.activation = keras.layers.PReLU
 
                 # apply to model
                 self.set_model()
@@ -49,14 +49,14 @@ How to create a customised model?
         class MyCustomisedModel(AbstractModelClass):
 
             def set_model(self):
-                x_input = keras.layers.Input(shape=(self.window_history_size + 1, 1, self.channels))
+                x_input = keras.layers.Input(shape=self.shape_inputs)
                 x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
                 x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
                 x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
                 x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(x_in)
                 x_in = keras.layers.Dense(16, name='{}_Dense_16'.format("major"))(x_in)
                 x_in = self.activation()(x_in)
-                x_in = keras.layers.Dense(self.window_lead_time, name='{}_Dense'.format("major"))(x_in)
+                x_in = keras.layers.Dense(self.shape_outputs, name='{}_Dense'.format("major"))(x_in)
                 out_main = self.activation()(x_in)
                 self.model = keras.Model(inputs=x_input, outputs=[out_main])
 
@@ -153,6 +153,7 @@ class AbstractModelClass(ABC):
                                           'target_tensors': None
                                           }
         self.__compile_options = self.__allowed_compile_options
+        self.__compile_options_is_set = False
         self.shape_inputs = shape_inputs
         self.shape_outputs = self.__extract_from_tuple(shape_outputs)
 
@@ -204,7 +205,8 @@ class AbstractModelClass(ABC):
     def compile_options(self) -> Callable:
         """
         The compile options property allows the user to use all keras.compile() arguments. They can ether be passed as
-        dictionary (1), as attribute, with compile_options=None (2) or as mixture of both of them (3).
+        dictionary (1), as attribute, without setting compile_options (2) or as mixture (partly defined as instance
+        attributes and partly parsing a dictionary) of both of them (3).
         The method will raise an Error when the same parameter is set differently.
 
         Example (1) Recommended (includes check for valid keywords which are used as args in keras.compile)
@@ -220,7 +222,6 @@ class AbstractModelClass(ABC):
                 self.optimizer = keras.optimizers.SGD()
                 self.loss = keras.losses.mean_squared_error
                 self.metrics = ["mse", "mae"]
-                self.compile_options = None # make sure to use this line
 
         Example (3)
         Correct:
@@ -245,6 +246,8 @@ class AbstractModelClass(ABC):
 
         :return:
         """
+        if self.__compile_options_is_set is False:
+            self.compile_options = None
         return self.__compile_options
 
     @compile_options.setter
@@ -274,6 +277,7 @@ class AbstractModelClass(ABC):
             else:
                 raise ValueError(
                     f"Got different values or arguments for same argument: self.{allow_k}={new_v_attr.__class__} and '{allow_k}': {new_v_dic.__class__}")
+        self.__compile_options_is_set = True
 
     @staticmethod
     def __extract_from_tuple(tup):
@@ -282,6 +286,11 @@ class AbstractModelClass(ABC):
 
     @staticmethod
     def __compare_keras_optimizers(first, second):
+        """
+        Compares if optimiser and all settings of the optimisers are exactly equal.
+
+        :return True if optimisers are interchangeable, or False if optimisers are distinguishable.
+        """
         if first.__class__ == second.__class__ and first.__module__ == 'keras.optimizers':
             res = True
             init = tf.global_variables_initializer()
@@ -688,3 +697,8 @@ class MyPaperModel(AbstractModelClass):
         self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
         self.compile_options = {"loss": [keras.losses.mean_squared_error, keras.losses.mean_squared_error],
                                 "metrics": ['mse', 'mea']}
+
+
+if __name__ == "__main__":
+    model = MyLittleModel([(1, 3, 10)], [2])
+    print(model.compile_options)