MyLittleModel consists now only on Dense layers, included bug fix /close #169

mlair/model_modules/model_class.py

@@ -351,9 +351,8 @@ class AbstractModelClass(ABC):
 
 class MyLittleModel(AbstractModelClass):
     """
-    A customised model with a 1x1 Conv, and 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the
-    output layer depending on the window_lead_time parameter. Dropout is used between the Convolution and the first
-    Dense layer.
+    A customised model 4 Dense layers (64, 32, 16, window_lead_time), where the last layer is the output layer depending
+    on the window_lead_time parameter.
     """
 
     def __init__(self, shape_inputs: list, shape_outputs: list):
@@ -382,13 +381,8 @@ class MyLittleModel(AbstractModelClass):
         """
         Build the model.
         """
-
-        # add 1 to window_size to include current time step t0
         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.Flatten(name='{}'.format("major"))(x_input)
         x_in = keras.layers.Dense(64, name='{}_Dense_64'.format("major"))(x_in)
         x_in = self.activation()(x_in)
         x_in = keras.layers.Dense(32, name='{}_Dense_32'.format("major"))(x_in)

mlair/run_modules/post_processing.py

@@ -81,16 +81,12 @@ class PostProcessing(RunEnvironment):
 
     def _run(self):
         # ols model
-        with TimeTracking():
         self.train_ols_model()
-            logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
-                         "skip train_ols_model() whenever it is possible to save time.")
 
         # forecasts
-        with TimeTracking():
         self.make_prediction()
-            logging.info("take a look on the next reported time measure. If this increases a lot, one should think to "
-                         "skip make_prediction() whenever it is possible to save time.")
+
+        # skill scores on test data
         self.calculate_test_score()
 
         # bootstraps