upadet examples

README.md

@@ -29,6 +29,7 @@ install the geo packages. For special instructions to install MLAir on the Jueli
     * or download the distribution file (?? .whl) and install it via `pip install <??>`. In this case, you can simply
       import MLAir in any python script inside your virtual environment using `import mlair`.
       
+
 # How to start with MLAir
 
 In this section, we show three examples how to work with MLAir. Note, that for these examples MLAir was installed using
@@ -51,11 +52,15 @@ INFO: DefaultWorkflow started
 INFO: ExperimentSetup started
 INFO: Experiment path is: /home/<usr>/mlair/testrun_network 
 ...
-INFO: load data for DEBW001 from JOIN 
+INFO: load data for DEBW107 from JOIN
+INFO: load data for DEBY081 from JOIN
+INFO: load data for DEBW013 from JOIN
+INFO: load data for DEBW076 from JOIN
+INFO: load data for DEBW087 from JOIN
 ...
 INFO: Training started
 ...
-INFO: DefaultWorkflow finished after 00:00:12 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 0:03:04 (hh:mm:ss)
 ```
 
 ## Example 2
@@ -82,10 +87,12 @@ INFO: ExperimentSetup started
 ...
 INFO: load data for DEBW030 from JOIN 
 INFO: load data for DEBW037 from JOIN 
+INFO: load data for DEBW031 from JOIN 
+INFO: load data for DEBW015 from JOIN 
 ...
 INFO: Training started
 ...
-INFO: DefaultWorkflow finished after 00:00:24 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:02:03 (hh:mm:ss)
 ```
 
 ## Example 3
@@ -107,15 +114,15 @@ window_history_size = 14
 mlair.run(stations=stations, 
           window_history_size=window_history_size, 
           create_new_model=False, 
-          trainable=False)
+          train_model=False)
 ```
 We can see from the terminal that no training was performed. Analysis is now made on the new stations.
 ```log
 INFO: DefaultWorkflow started
 ...
-INFO: No training has started, because trainable parameter was false. 
+INFO: No training has started, because train_model parameter was false. 
 ...
-INFO: DefaultWorkflow finished after 00:00:06 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 0:01:27 (hh:mm:ss)
 ```
 
 
@@ -222,18 +229,14 @@ behaviour.
 
 ```python
 from mlair import AbstractModelClass
-import keras
 
 class MyCustomisedModel(AbstractModelClass):
 
     def __init__(self, input_shape: list, output_shape: list):
 
+        # set attributes shape_inputs and shape_outputs
         super().__init__(input_shape[0], output_shape[0])
 
-        # settings
-        self.dropout_rate = 0.1
-        self.activation = keras.layers.PReLU
-
         # apply to model
         self.set_model()
         self.set_compile_options()
@@ -254,34 +257,36 @@ class MyCustomisedModel(AbstractModelClass):
   `self._output_shape` and storing the model as `self.model`.
 
 ```python
+import keras
+from keras.layers import PReLU, Input, Conv2D, Flatten, Dropout, Dense
+
 class MyCustomisedModel(AbstractModelClass):
 
     def set_model(self):
-        x_input = keras.layers.Input(shape=self._input_shape)
-        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._output_shape, name='{}_Dense'.format("major"))(x_in)
-        out_main = self.activation()(x_in)
-        self.model = keras.Model(inputs=x_input, outputs=[out_main])
+        x_input = Input(shape=self._input_shape)
+        x_in = Conv2D(4, (1, 1))(x_input)
+        x_in = PReLU()(x_in)
+        x_in = Flatten()(x_in)
+        x_in = Dropout(0.1)(x_in)
+        x_in = Dense(16)(x_in)
+        x_in = PReLU()(x_in)
+        x_in = Dense(self._output_shape)(x_in)
+        out = PReLU()(x_in)
+        self.model = keras.Model(inputs=x_input, outputs=[out])
 ```
 
 * Your are free how to design your model. Just make sure to save it in the class attribute model.
 * Additionally, set your custom compile options including the loss definition.
 
 ```python
+from keras.losses import mean_squared_error as mse
+
 class MyCustomisedModel(AbstractModelClass):
 
     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.loss = mse
         self.compile_options = {"metrics": ["mse", "mae"]}
 ```
 
@@ -303,6 +308,15 @@ class MyCustomisedModel(AbstractModelClass):
         self.compile_options = {"optimizer" = keras.optimizers.Adam()}
     ```
     
+### How to plug in the customised model into the workflow?
+* Make use of the `model` argument and pass `MyCustomisedModel` when instantiating a workflow.
+```python
+from mlair.workflows import DefaultWorkflow
+
+workflow = DefaultWorkflow(model=MyCustomisedModel)
+workflow.run()
+```
+
 
 ## Specials for Branched Models